diff --git a/.github/workflows/testing-pinned.yaml b/.github/workflows/testing-pinned.yaml index 1ac875a8..dfa498cb 100644 --- a/.github/workflows/testing-pinned.yaml +++ b/.github/workflows/testing-pinned.yaml @@ -4,6 +4,7 @@ on: pull_request: branches: - main + - pourbaix_pyEQL_v1.5 paths-ignore: - CHANGELOG.md - AUTHORS.md @@ -39,7 +40,11 @@ jobs: run: | python -m pip install --upgrade pip pip install -r requirements.txt - pip install -e . --no-deps + pip install -e ".[testing]" + + - name: Install pourbaix deps + if: matrix.python-version != '3.10' + run: pip install -e ".[pourbaix]" - name: Run tests run: | diff --git a/.github/workflows/testing.yaml b/.github/workflows/testing.yaml index 2b5934f1..f50e78b5 100644 --- a/.github/workflows/testing.yaml +++ b/.github/workflows/testing.yaml @@ -37,6 +37,10 @@ jobs: python -m pip install --upgrade pip pip install -e ".[testing]" + - name: Install pourbaix deps + if: matrix.python-version != '3.10' + run: pip install -e ".[pourbaix]" + - name: List installed requirements run: | pip freeze diff --git a/pyproject.toml b/pyproject.toml index 45bf9986..13e52494 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -48,6 +48,9 @@ testing = [ "ruff>0.0.100", "tox<4", ] +pourbaix = [ + "mp-api>=0.45.0", + ] docs = [ "sphinx>=3.2.1", "sphinx-rtd-theme", @@ -176,5 +179,5 @@ enable_error_code = ["ignore-without-code", "redundant-expr", "truthy-bool"] warn_unreachable = true [tool.codespell] -ignore-words-list = "nd,formate,Te,CaCl,CaF,ore" +ignore-words-list = "nd,formate,Te,CaCl,CaF,ore, teh,hte,hsi" skip = "tests/test_files/*,src/pyEQL/database/*" diff --git a/src/pyEQL/pourbaix/MP2020Compatibility.yaml b/src/pyEQL/pourbaix/MP2020Compatibility.yaml new file mode 100644 index 00000000..8c17de15 --- /dev/null +++ b/src/pyEQL/pourbaix/MP2020Compatibility.yaml @@ -0,0 +1,80 @@ +Name: MP2020 +Corrections: + # Energy corrections in eV/atom + GGAUMixingCorrections: + # Composition-based corrections applied to transition metal oxides + # and fluorides to make GGA and GGA+U energies compatible + # when compat_type = "Advanced" (default) + O: + V: -1.7 + Cr: -1.999 + Mn: -1.668 + Fe: -2.256 + Co: -1.638 + Ni: -2.541 + W: -4.438 + Mo: -3.202 + F: + V: -1.7 + Cr: -1.999 + Mn: -1.668 + Fe: -2.256 + Co: -1.638 + Ni: -2.541 + W: -4.438 + Mo: -3.202 + CompositionCorrections: + # Composition-based corrections applied to any compound containing + # these species as anions + oxide: -0.687 + peroxide: -0.465 + superoxide: -0.161 + S: -0.503 + F: -0.462 + Cl: -0.614 + Br: -0.534 + I: -0.379 + N: -0.361 + Se: -0.472 + Si: 0.071 + Sb: -0.192 + Te: -0.422 + H: -0.179 + ozonide: 0 +Uncertainties: + # Uncertainties corresponding to each energy correction (eV/atom) + GGAUMixingCorrections: + O: + V: 0.0064 + Cr: 0.0108 + Mn: 0.0053 + Fe: 0.0101 + Co: 0.006 + Ni: 0.0107 + W: 0.0253 + Mo: 0.0089 + F: + V: 0.0064 + Cr: 0.0108 + Mn: 0.0053 + Fe: 0.0101 + Co: 0.006 + Ni: 0.0107 + W: 0.0253 + Mo: 0.0089 + CompositionCorrections: + oxide: 0.002 + peroxide: 0.0172 + superoxide: 0.0075 + S: 0.0093 + F: 0.0026 + Cl: 0.0018 + Br: 0.0026 + I: 0.0055 + N: 0.0093 + Se: 0.0341 + Si: 0.0165 + Sb: 0.0089 + Te: 0.0262 + H: 0.0013 + ozonide: 0 diff --git a/src/pyEQL/pourbaix/NBS_Tables_Library.xlsx b/src/pyEQL/pourbaix/NBS_Tables_Library.xlsx new file mode 100644 index 00000000..e67e4e46 Binary files /dev/null and b/src/pyEQL/pourbaix/NBS_Tables_Library.xlsx differ diff --git a/src/pyEQL/pourbaix/__init__.py b/src/pyEQL/pourbaix/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/src/pyEQL/pourbaix/compatibility.py b/src/pyEQL/pourbaix/compatibility.py new file mode 100644 index 00000000..7b2b6e4c --- /dev/null +++ b/src/pyEQL/pourbaix/compatibility.py @@ -0,0 +1,1742 @@ +"""This module implements Compatibility corrections for mixing runs of different +functionals. +""" + +from __future__ import annotations + +import abc +import copy +import os +import warnings +from collections import defaultdict +from typing import TYPE_CHECKING, TypeAlias, cast + +import numpy as np +from joblib import Parallel, delayed +from monty.design_patterns import cached_class +from monty.json import MSONable +from monty.serialization import loadfn +from pymatgen.analysis.structure_analyzer import oxide_type, sulfide_type +from pymatgen.core import SETTINGS, Composition, Element +from pymatgen.entries.computed_entries import ( + CompositionEnergyAdjustment, + ComputedEntry, + ComputedStructureEntry, + ConstantEnergyAdjustment, + EnergyAdjustment, + TemperatureEnergyAdjustment, +) +from pymatgen.io.vasp.sets import MPRelaxSet, VaspInputSet +from pymatgen.util.due import Doi, due +from pymatgen.util.joblib import set_python_warnings, tqdm_joblib +from tqdm import tqdm +from uncertainties import ufloat + +if TYPE_CHECKING: + from collections.abc import Sequence + from typing import Any, Literal + + from pymatgen.util.typing import CompositionLike, PathLike + + +__author__ = "Amanda Wang, Ryan Kingsbury, Shyue Ping Ong, Anubhav Jain, Stephen Dacek, Sai Jayaraman" +__copyright__ = "Copyright 2012-2020, The Materials Project" +__version__ = "1.0" +__maintainer__ = "Shyue Ping Ong" +__email__ = "shyuep@gmail.com" +__date__ = "April 2020" + +MODULE_DIR = os.path.dirname(os.path.abspath(__file__)) +MU_H2O = -2.4583 # Free energy of formation of water, eV/H2O, used by MaterialsProjectAqueousCompatibility +MP2020_COMPAT_CONFIG = loadfn(f"{MODULE_DIR}/MP2020Compatibility.yaml") +# MP_COMPAT_CONFIG = loadfn(f"{MODULE_DIR}/MPCompatibility.yaml") + +# This was compiled by cross-referencing structures in Materials Project from exp_compounds.json.gz +# used in the fitting of the MP2020 correction scheme, and applying the BVAnalyzer algorithm to +# determine oxidation state. O and S are not included since these are treated separately. +MP2020_ANION_OXIDATION_STATE_RANGES: dict[str, tuple[int, int]] = { + "Br": (-1, -1), + "Cl": (-1, -1), + "F": (-1, -1), + "H": (-1, -1), + "I": (-1, -1), + "N": (-3, -2), + "Sb": (-3, -2), + "Se": (-2, -1), + "Si": (-4, -1), + "Te": (-2, -1), +} + +# Ping @janosh @rkingsbury on GitHub if this fails +if ( + MP2020_COMPAT_CONFIG["Corrections"]["GGAUMixingCorrections"]["O"] + != MP2020_COMPAT_CONFIG["Corrections"]["GGAUMixingCorrections"]["F"] +): + raise RuntimeError("MP2020Compatibility.yaml expected to have the same Hubbard U corrections for O and F") + +AnyComputedEntry: TypeAlias = ComputedEntry | ComputedStructureEntry + + +class CompatibilityError(Exception): + """Exception class for Compatibility. Raised by attempting correction + on incompatible calculation. + """ + + +class Correction(abc.ABC): + """A Correction class is a pre-defined scheme for correction a computed + entry based on the type and chemistry of the structure and the + calculation parameters. All Correction classes must implement a + correct_entry method. + """ + + @abc.abstractmethod + def get_correction(self, entry: AnyComputedEntry) -> EnergyAdjustment: + """Get correction and uncertainty for a single entry. + + Args: + entry: A ComputedEntry object. + + Returns: + The energy correction to be applied and the uncertainty of the correction. + + Raises: + CompatibilityError if entry is not compatible. + """ + raise NotImplementedError + + def correct_entry(self, entry): + """Corrects a single entry. + + Args: + entry: A ComputedEntry object. + + Returns: + An processed entry. + + Raises: + CompatibilityError if entry is not compatible. + """ + new_corr = self.get_correction(entry) + old_std_dev = entry.correction_uncertainty + if np.isnan(old_std_dev): + old_std_dev = 0 + old_corr = ufloat(entry.correction, old_std_dev) + updated_corr = new_corr + old_corr + + # if there are no error values available for the corrections applied, + # set correction uncertainty to not a number + uncertainty = np.nan if updated_corr.nominal_value != 0 and updated_corr.std_dev == 0 else updated_corr.std_dev + + entry.energy_adjustments.append(ConstantEnergyAdjustment(updated_corr.nominal_value, uncertainty)) + + return entry + + +class PotcarCorrection(Correction): + """Check that POTCARs are valid within a pre-defined input set. This + ensures that calculations performed using different InputSets are not + compared against each other. + + Entry.parameters must contain a "potcar_symbols" key that is a list of + all POTCARs used in the run. Again, using the example of an Fe2O3 run + using Materials Project parameters, this would look like + entry.parameters["potcar_symbols"] = ['PAW_PBE Fe_pv 06Sep2000', + 'PAW_PBE O 08Apr2002']. + """ + + def __init__( + self, + input_set: type[VaspInputSet], + check_potcar: bool = True, + check_hash: bool = False, + ) -> None: + """ + Args: + input_set (InputSet): object used to generate the runs (used to check + for correct potcar symbols). + check_potcar (bool): If False, bypass the POTCAR check altogether. Defaults to True. + Can also be disabled globally by running `pmg config --add PMG_POTCAR_CHECKS false`. + check_hash (bool): If True, uses the potcar hash to check for valid + potcars. If false, uses the potcar symbol (less reliable). Defaults to False. + + Raises: + ValueError: if check_potcar=True and entry does not contain "potcar_symbols" key. + """ + potcar_settings = input_set.CONFIG["POTCAR"] + if isinstance(list(potcar_settings.values())[-1], dict): + self.valid_potcars = { + key: dct.get("hash" if check_hash else "symbol") for key, dct in potcar_settings.items() + } + else: + if check_hash: + raise ValueError("Cannot check hashes of potcars, since hashes are not included in the entry.") + self.valid_potcars = potcar_settings + + self.input_set = input_set + self.check_hash = check_hash + self.check_potcar = check_potcar + + def __str__(self) -> str: + return f"{self.input_set.__name__} Potcar Correction" + + def get_correction(self, entry: AnyComputedEntry) -> ufloat: + """ + Args: + entry (AnyComputedEntry): ComputedEntry or ComputedStructureEntry. + + Raises: + ValueError: If entry does not contain "potcar_symbols" key. + CompatibilityError: If entry has wrong potcar hash/symbols. + + Returns: + ufloat: 0.0 +/- 0.0 (from uncertainties package) + """ + if SETTINGS.get("PMG_POTCAR_CHECKS") is False or not self.check_potcar: + return ufloat(0.0, 0.0) + + potcar_spec = entry.parameters.get("potcar_spec") + if self.check_hash: + if potcar_spec: + psp_settings = {dct.get("hash") for dct in potcar_spec if dct} + else: + raise ValueError("Cannot check hash without potcar_spec field") + elif potcar_spec: + psp_settings = {dct.get("titel").split()[1] for dct in potcar_spec if dct} # codespell:ignore=titel + else: + psp_settings = {sym.split()[1] for sym in entry.parameters["potcar_symbols"] if sym} + + expected_psp = {self.valid_potcars.get(el.symbol) for el in entry.elements} + if expected_psp != psp_settings: + raise CompatibilityError(f"Incompatible POTCAR {psp_settings}, expected {expected_psp}") + return ufloat(0.0, 0.0) + + +@cached_class +class GasCorrection(Correction): + """Correct gas energies to obtain the right formation energies. Note that + this depends on calculations being run within the same input set. + Used by legacy MaterialsProjectCompatibility and MITCompatibility. + """ + + def __init__(self, config_file: PathLike) -> None: + """ + Args: + config_file (PathLike): Path to the selected compatibility.yaml config file. + """ + config = loadfn(config_file) + self.name = config["Name"] + self.cpd_energies = config["Advanced"]["CompoundEnergies"] + + def __str__(self) -> str: + return f"{self.name} Gas Correction" + + def get_correction(self, entry: ComputedEntry | ComputedStructureEntry) -> ufloat: + """ + Args: + entry: A ComputedEntry/ComputedStructureEntry. + + Returns: + ufloat: Correction. + """ + comp = entry.composition + # Set error to 0 because old MPCompatibility doesn't have errors + correction = ufloat(0.0, 0.0) + + rform = entry.reduced_formula + if rform in self.cpd_energies: + correction += self.cpd_energies[rform] * comp.num_atoms - entry.uncorrected_energy + + return correction + + +@cached_class +class AnionCorrection(Correction): + """Correct anion energies to obtain the right formation energies. Note that + this depends on calculations being run within the same input set. + + Used by legacy MaterialsProjectCompatibility and MITCompatibility. + """ + + def __init__(self, config_file: PathLike, correct_peroxide: bool = True) -> None: + """ + Args: + config_file (PathLike): Path to the selected compatibility.yaml config file. + correct_peroxide (bool): Whether peroxide/superoxide/ozonide + corrections are to be applied or not. + """ + config = loadfn(config_file) + self.oxide_correction = config["OxideCorrections"] + self.sulfide_correction = config.get("SulfideCorrections", defaultdict(float)) + self.name = config["Name"] + self.correct_peroxide = correct_peroxide + + def __str__(self) -> str: + return f"{self.name} Anion Correction" + + def get_correction(self, entry: ComputedEntry | ComputedStructureEntry) -> ufloat: + """ + Args: + entry: A ComputedEntry/ComputedStructureEntry. + + Returns: + ufloat: Correction. + """ + comp = entry.composition + if len(comp) == 1: # Skip element entry + return ufloat(0.0, 0.0) + + correction = ufloat(0.0, 0.0) + + # Check for sulfide corrections + if Element("S") in comp: + sf_type = "sulfide" + + if entry.data.get("sulfide_type"): + sf_type = entry.data["sulfide_type"] + elif hasattr(entry, "structure"): + warnings.warn(sf_type, stacklevel=2) + sf_type = sulfide_type(entry.structure) + + # use the same correction for polysulfides and sulfides + if sf_type == "polysulfide": + sf_type = "sulfide" + + if sf_type in self.sulfide_correction: + correction += self.sulfide_correction[sf_type] * comp["S"] + + # Check for oxide, peroxide, superoxide, and ozonide corrections. + if Element("O") in comp: + if self.correct_peroxide: + if entry.data.get("oxide_type"): + if entry.data["oxide_type"] in self.oxide_correction: + ox_corr = self.oxide_correction[entry.data["oxide_type"]] + correction += ox_corr * comp["O"] + if entry.data["oxide_type"] == "hydroxide": + ox_corr = self.oxide_correction["oxide"] + correction += ox_corr * comp["O"] + + elif hasattr(entry, "structure"): + ox_type, n_bonds = cast( + "tuple[str, int]", + oxide_type(entry.structure, 1.05, return_nbonds=True), + ) + if ox_type in self.oxide_correction: + correction += self.oxide_correction[ox_type] * n_bonds + elif ox_type == "hydroxide": + correction += self.oxide_correction["oxide"] * comp["O"] + else: + warnings.warn( + "No structure or oxide_type parameter present. Note that peroxide/superoxide corrections " + "are not as reliable and relies only on detection of special formulas, e.g. Li2O2.", + stacklevel=2, + ) + rform = entry.reduced_formula + if rform in UCorrection.common_peroxides: + correction += self.oxide_correction["peroxide"] * comp["O"] + elif rform in UCorrection.common_superoxides: + correction += self.oxide_correction["superoxide"] * comp["O"] + elif rform in UCorrection.ozonides: + correction += self.oxide_correction["ozonide"] * comp["O"] + elif Element("O") in comp.elements and len(comp.elements) > 1: + correction += self.oxide_correction["oxide"] * comp["O"] + else: + correction += self.oxide_correction["oxide"] * comp["O"] + + return correction + + +@cached_class +class AqueousCorrection(Correction): + """This class implements aqueous phase compound corrections for elements + and H2O. + + Used only by MITAqueousCompatibility. + """ + + def __init__(self, config_file: PathLike, error_file: PathLike | None = None) -> None: + """ + Args: + config_file (PathLike): Path to the selected compatibility.yaml config file. + error_file (PathLike): Path to the selected compatibilityErrors.yaml config file. + """ + config = loadfn(config_file) + self.cpd_energies = config["AqueousCompoundEnergies"] + # there will either be a CompositionCorrections OR an OxideCorrections key, + # but not both, depending on the compatibility scheme we are using. + # MITCompatibility only uses OxideCorrections, and hence self.comp_correction is none. + self.comp_correction = config.get("CompositionCorrections", defaultdict(float)) + self.oxide_correction = config.get("OxideCorrections", defaultdict(float)) + self.name = config["Name"] + if error_file: + e = loadfn(error_file) + self.cpd_errors = e.get("AqueousCompoundEnergies", defaultdict(float)) + else: + self.cpd_errors = defaultdict(float) + + def __str__(self) -> str: + return f"{self.name} Aqueous Correction" + + def get_correction(self, entry: ComputedEntry | ComputedStructureEntry) -> ufloat: + """ + Args: + entry: A ComputedEntry/ComputedStructureEntry. + + Returns: + ufloat: Correction with uncertainty. + """ + comp = entry.composition + rform = comp.reduced_formula + cpd_energies = self.cpd_energies + + correction = ufloat(0.0, 0.0) + + if rform in cpd_energies: + if rform in {"H2", "H2O"}: + corr = cpd_energies[rform] * comp.num_atoms - entry.uncorrected_energy - entry.correction + else: + corr = cpd_energies[rform] * comp.num_atoms + + err = self.cpd_errors[rform] * comp.num_atoms + correction += ufloat(corr, err) + + # if rform != "H2O": + # # if the composition contains water molecules (e.g. FeO.nH2O), + # # correct the gibbs free energy such that the waters are assigned energy=MU_H2O + # # in other words, we assume that the DFT energy of such a compound is really + # # a superposition of the "real" solid DFT energy (FeO in this case) and the free + # # energy of some water molecules + # # e.g. that E_FeO.nH2O = E_FeO + n * g_H2O + # # so, to get the most accurate gibbs free energy, we want to replace + # # g_FeO.nH2O = E_FeO.nH2O + dE_Fe + (n+1) * dE_O + 2n dE_H + # # with + # # g_FeO = E_FeO.nH2O + dE_Fe + dE_O + n g_H2O + # # where E is DFT energy, dE is an energy correction, and g is gibbs free energy + # # This means we have to 1) remove energy corrections associated with H and O in water + # # and then 2) remove the free energy of the water molecules + + # nH2O = int(min(comp["H"] / 2.0, comp["O"])) # only count whole water molecules + # if nH2O > 0: + # # first, remove any H or O corrections already applied to H2O in the + # # formation energy so that we don't double count them + # # No. of H atoms not in a water + # correction -= ufloat((comp["H"] - nH2O / 2) * self.comp_correction["H"], 0.0) + # # No. of O atoms not in a water + # correction -= ufloat( + # (comp["O"] - nH2O) * (self.comp_correction["oxide"] + self.oxide_correction["oxide"]), + # 0.0, + # ) + # # next, add MU_H2O for each water molecule present + # correction += ufloat(-1 * MU_H2O * nH2O, 0.0) + # # correction += 0.5 * 2.46 * nH2O # this is the old way this correction was calculated + + return correction + + +@cached_class +class UCorrection(Correction): + """This class implements the GGA/GGA+U mixing scheme, which allows mixing of + entries. Entry.parameters must contain a "hubbards" key which is a dict + of all non-zero Hubbard U values used in the calculation. For example, + if you ran a Fe2O3 calculation with Materials Project parameters, + this would look like entry.parameters["hubbards"] = {"Fe": 5.3} + If the "hubbards" key is missing, a GGA run is assumed. + + It should be noted that ComputedEntries assimilated using the + pymatgen.apps.borg package and obtained via the MaterialsProject REST + interface using the pymatgen.matproj.rest package will automatically have + these fields populated. + """ + + common_peroxides = ( + "Li2O2", + "Na2O2", + "K2O2", + "Cs2O2", + "Rb2O2", + "BeO2", + "MgO2", + "CaO2", + "SrO2", + "BaO2", + ) + common_superoxides = ("LiO2", "NaO2", "KO2", "RbO2", "CsO2") + ozonides = ("LiO3", "NaO3", "KO3", "NaO5") + + def __init__( + self, + config_file: PathLike, + input_set, + compat_type: Literal["GGA", "Advanced"], + error_file: PathLike | None = None, + ) -> None: + """ + Args: + config_file (PathLike): Path to the selected compatibility.yaml config file. + input_set: InputSet object to check for the +U settings. + compat_type ("GGA" | "Advanced"): "GGA" means all GGA+U + entries are excluded. "Advanced" means mixing scheme is + implemented to make entries compatible with each other, + but entries which are supposed to be done in GGA+U will have the + equivalent GGA entries excluded. For example, Fe oxides should + have a U value under the Advanced scheme. A GGA Fe oxide run + will therefore be excluded under the scheme. + error_file (PathLike): Path to the selected compatibilityErrors.yaml config file. + """ + if compat_type not in {"GGA", "Advanced"}: + raise CompatibilityError(f"Invalid {compat_type=}") + + config = loadfn(config_file) + + self.input_set = input_set + if compat_type == "Advanced": + self.u_settings = self.input_set.CONFIG["INCAR"]["LDAUU"] + self.u_corrections = config["Advanced"]["UCorrections"] + else: + self.u_settings = {} + self.u_corrections = {} + + self.name = config["Name"] + self.compat_type = compat_type + + if error_file: + err = loadfn(error_file) + self.u_errors = err["Advanced"]["UCorrections"] + else: + self.u_errors = {} + + def __str__(self) -> str: + return f"{self.name} {self.compat_type} Correction" + + def get_correction(self, entry: ComputedEntry | ComputedStructureEntry) -> ufloat: + """ + Args: + entry: A ComputedEntry/ComputedStructureEntry. + + Returns: + ufloat: Correction with Uncertainty. + """ + calc_u = entry.parameters.get("hubbards") or defaultdict(int) + comp = entry.composition + + elements = sorted((el for el in comp.elements if comp[el] > 0), key=lambda el: el.X) + most_electro_neg = elements[-1].symbol + correction = ufloat(0.0, 0.0) + + u_corr = self.u_corrections.get(most_electro_neg, {}) + u_settings = self.u_settings.get(most_electro_neg, {}) + u_errors = self.u_errors.get(most_electro_neg, defaultdict(float)) + + for el in comp.elements: + sym = el.symbol + # Check for bad U values + if calc_u.get(sym, 0) != u_settings.get(sym, 0): + raise CompatibilityError(f"Invalid U value of {calc_u.get(sym, 0)} on {sym}") + if sym in u_corr: + correction += ufloat(u_corr[sym], u_errors[sym]) * comp[el] + + return correction + + +class Compatibility(MSONable, abc.ABC): + """Abstract Compatibility class, not intended for direct use. + Compatibility classes are used to correct the energies of an entry or a set + of entries. All Compatibility classes must implement get_adjustments() method. + """ + + @abc.abstractmethod + def get_adjustments(self, entry: AnyComputedEntry) -> list[EnergyAdjustment]: + """Get the energy adjustments for a ComputedEntry. + + This method must generate a list of EnergyAdjustment objects + of the appropriate type (constant, composition-based, or temperature-based) + to be applied to the ComputedEntry, and must raise a CompatibilityError + if the entry is not compatible. + + Args: + entry: A ComputedEntry object. + + Returns: + list[EnergyAdjustment]: A list of EnergyAdjustment to be applied to the + Entry. + + Raises: + CompatibilityError if the entry is not compatible + """ + raise NotImplementedError + + def process_entry( + self, + entry: ComputedEntry, + inplace: bool = True, + **kwargs, + ) -> ComputedEntry | None: + """Process a single entry with the chosen Corrections. + Note that this method may change the original entry. + + Args: + entry (ComputedEntry): A ComputedEntry object. + inplace (bool): Whether to adjust the entry in place. Defaults to True. + **kwargs: Will be passed to process_entries(). + + Returns: + An adjusted entry if entry is compatible, else None. + """ + if not inplace: + entry = copy.deepcopy(entry) + + _entry: tuple[ComputedEntry, bool] | None = self._process_entry_inplace(entry, **kwargs) + + return _entry[0] if _entry is not None else None + + def _process_entry_inplace( + self, + entry: AnyComputedEntry, + clean: bool = True, + on_error: Literal["ignore", "warn", "raise"] = "ignore", + ) -> tuple[ComputedEntry, bool] | None: + """Process a single entry with the chosen Corrections. + Note that this method will change the original entry. + + Args: + entry (AnyComputedEntry): An AnyComputedEntry object. + clean (bool): Whether to remove any previously-applied energy adjustments. + If True, all EnergyAdjustment are removed prior to processing the Entry. + Defaults to True. + on_error ("ignore" | "warn" | "raise"): What to do when get_adjustments(entry) + raises CompatibilityError. Defaults to "ignore". + + Returns: + tuple[AnyComputedEntry, ignore_entry (bool)] if entry is compatible, else None. + """ + ignore_entry: bool = False + # If clean, remove all previous adjustments from the entry + if clean: + entry.energy_adjustments = [] + + # Get the energy adjustments + try: + adjustments: list[EnergyAdjustment] = self.get_adjustments(entry) + + except CompatibilityError as exc: + if on_error == "raise": + raise + if on_error == "warn": + warnings.warn(str(exc), stacklevel=2) + return None + + for e_adj in adjustments: + # Check if this correction already been applied + if (e_adj.name, e_adj.cls, e_adj.value) in [ + (ea2.name, ea2.cls, ea2.value) for ea2 in entry.energy_adjustments + ]: + # We already applied this exact correction. Do nothing. + pass + + elif (e_adj.name, e_adj.cls) in [(ea2.name, ea2.cls) for ea2 in entry.energy_adjustments]: + # We already applied a correction with the same name + # but a different value. Something is wrong. + ignore_entry = True + warnings.warn( + f"Entry {entry.entry_id} already has an energy adjustment called {e_adj.name}, but its " + f"value differs from the value of {e_adj.value:.3f} calculated here. This " + "Entry will be discarded.", + stacklevel=2, + ) + + else: + # Add the correction to the energy_adjustments list + entry.energy_adjustments.append(e_adj) + + return entry, ignore_entry + + def process_entries( + self, + entries: AnyComputedEntry | list[AnyComputedEntry], + clean: bool = True, + verbose: bool = False, + inplace: bool = True, + n_workers: int = 1, + on_error: Literal["ignore", "warn", "raise"] = "ignore", + ) -> list[AnyComputedEntry]: + """Process a sequence of entries with the chosen Compatibility scheme. + + Warning: This method changes entries in place! All changes can be undone and original entries + restored by setting entry.energy_adjustments = []. + + Args: + entries (AnyComputedEntry | list[AnyComputedEntry]): A sequence of + Computed(Structure)Entry objects. + clean (bool): Whether to remove any previously-applied energy adjustments. + If True, all EnergyAdjustment are removed prior to processing the Entry. + Defaults to True. + verbose (bool): Whether to display progress bar for processing multiple entries. + Defaults to False. + inplace (bool): Whether to adjust input entries in place. Defaults to True. + n_workers (int): Number of workers to use for parallel processing. Defaults to 1. + on_error ('ignore' | 'warn' | 'raise'): What to do when get_adjustments(entry) + raises CompatibilityError. Defaults to 'ignore'. + + Returns: + list[AnyComputedEntry]: Adjusted entries. Entries in the original list incompatible with + chosen correction scheme are excluded from the returned list. + """ + # if single entry convert to list + if isinstance(entries, ComputedEntry): # True for ComputedStructureEntry too + entries = [entries] + + processed_entry_list: list[AnyComputedEntry] = [] + + # if inplace = False, process entries on a copy + if not inplace: + entries = copy.deepcopy(entries) + + if n_workers == 1: + for entry in tqdm(entries, disable=not verbose): + result = self._process_entry_inplace(entry, clean, on_error) + if result is None: + continue + entry, ignore_entry = result + if not ignore_entry: + processed_entry_list.append(entry) + elif not inplace: + # set python warnings to ignore otherwise warnings will be printed multiple times + with ( + tqdm_joblib(tqdm(total=len(entries), disable=not verbose)), + set_python_warnings("ignore"), + ): + results = Parallel(n_jobs=n_workers)( + delayed(self._process_entry_inplace)(entry, clean, on_error) for entry in entries + ) + for result in results: + if result is None: + continue + entry, ignore_entry = result + if not ignore_entry: + processed_entry_list.append(entry) + else: + raise ValueError("Parallel processing is not possible with for 'inplace=True'") + + return processed_entry_list + + @staticmethod + def explain(entry: ComputedEntry) -> None: + """Print an explanation of the energy adjustments applied by the + Compatibility class. Inspired by the "explain" methods in many database + methodologies. + + Args: + entry: A ComputedEntry. + """ + print( + f"The uncorrected energy of {entry.composition} is {entry.uncorrected_energy:.3f} eV " + f"({entry.uncorrected_energy / entry.composition.num_atoms:.3f} eV/atom)." + ) + + if len(entry.energy_adjustments) > 0: + print("The following energy adjustments have been applied to this entry:") + for adj in entry.energy_adjustments: + print(f"\t\t{adj.name}: {adj.value:.3f} eV ({adj.value / entry.composition.num_atoms:.3f} eV/atom)") + elif entry.correction == 0: + print("No energy adjustments have been applied to this entry.") + + print(f"The final energy after adjustments is {entry.energy:.3f} eV ({entry.energy_per_atom:.3f} eV/atom).") + + +class CorrectionsList(Compatibility): + """The CorrectionsList class combines a list of corrections to be applied to + an entry or a set of entries. Note that some of the Corrections have + interdependencies. For example, PotcarCorrection must always be used + before any other compatibility. Also, AnionCorrection("MP") must be used + with PotcarCorrection("MP") (similarly with "MIT"). Typically, + you should use the specific MaterialsProjectCompatibility and + MITCompatibility subclasses instead. + """ + + def __init__( + self, + corrections: Sequence[Correction], + run_types: list[Literal["GGA", "GGA+U", "PBE", "PBE+U"]] | None = None, + ) -> None: + """ + Args: + corrections (Sequence[Correction]): Corrections to apply. + run_types (list[str]): Valid DFT run types for this correction scheme. + Entries with run type other than those in this list will be excluded + from the list returned by process_entries. The default value captures + both GGA and GGA+U run types historically used by the Materials Project. + """ + if run_types is None: + run_types = ["GGA", "GGA+U", "PBE", "PBE+U"] + self.corrections = corrections + self.run_types = run_types + super().__init__() + + def get_adjustments(self, entry: AnyComputedEntry) -> list[EnergyAdjustment]: + """Get the list of energy adjustments to be applied to an entry.""" + adjustment_list = [] + if entry.parameters.get("run_type") not in self.run_types: + raise CompatibilityError( + f"Entry {entry.entry_id} has invalid run type {entry.parameters.get('run_type')}. " + f"Must be GGA or GGA+U. Discarding." + ) + + corrections, uncertainties = self.get_corrections_dict(entry) + + for k, v in corrections.items(): + uncertainty = np.nan if v != 0 and uncertainties[k] == 0 else uncertainties[k] + adjustment_list.append(ConstantEnergyAdjustment(v, uncertainty=uncertainty, name=k, cls=self.as_dict())) + + return adjustment_list + + def get_corrections_dict( + self, + entry: AnyComputedEntry, + ) -> tuple[dict[str, float], dict[str, float]]: + """Get the correction values and uncertainties applied to a particular entry. + + Args: + entry: An AnyComputedEntry object. + + Returns: + tuple[dict[str, float], dict[str, float]]: Map from correction names to values + (1st) and uncertainties (2nd). + """ + corrections = {} + uncertainties = {} + for c in self.corrections: + val = c.get_correction(entry) + if val != 0: + corrections[str(c)] = val.nominal_value + uncertainties[str(c)] = val.std_dev + return corrections, uncertainties + + def get_explanation_dict(self, entry: ComputedEntry) -> dict[str, Any]: + """Explain the corrections applied for a given compatibility scheme. Inspired by the + "explain" methods in many database methodologies. + + Args: + entry: A ComputedEntry. + + Returns: + dict[str, str | float | list[dict[str, Union[str, float]]]: of the form + {"Compatibility": "string", + "Uncorrected_energy": float, + "Corrected_energy": float, + "correction_uncertainty:" float, + "Corrections": [{"Name of Correction": { + "Value": float, "Explanation": "string", "Uncertainty": float}]} + """ + corr_entry = self.process_entry(entry) + uncorrected_energy = (corr_entry or entry).uncorrected_energy + corrected_energy = corr_entry.energy if corr_entry else None + correction_uncertainty = corr_entry.correction_uncertainty if corr_entry else None + + dct = { + "compatibility": type(self).__name__, + "uncorrected_energy": uncorrected_energy, + "corrected_energy": corrected_energy, + "correction_uncertainty": correction_uncertainty, + } + corrections = [] + corr_dict, uncer_dict = self.get_corrections_dict(entry) + for c in self.corrections: + if corr_dict.get(str(c), 0) != 0 and uncer_dict.get(str(c), 0) == 0: + uncer = np.nan + else: + uncer = uncer_dict.get(str(c), 0) + + if c.__doc__ is None: + raise RuntimeError("__doc__ of some correction is None.") + cd = { + "name": str(c), + "description": c.__doc__.split("Args")[0].strip(), + "value": corr_dict.get(str(c), 0), + "uncertainty": uncer, + } + corrections.append(cd) + dct["corrections"] = corrections + return dct + + def explain(self, entry: ComputedEntry) -> None: + """Print an explanation of the corrections that are being applied for a + given compatibility scheme. Inspired by the "explain" methods in many + database methodologies. + + Args: + entry: A ComputedEntry. + """ + dct = self.get_explanation_dict(entry) + print(f"The uncorrected value of the energy of {entry.composition} is {dct['uncorrected_energy']:f} eV") + print(f"The following corrections / screening are applied for {dct['compatibility']}:\n") + for corr in dct["corrections"]: + print(f"{corr['name']} correction: {corr['description']}\n") + print(f"For the entry, this correction has the value {corr['value']:f} eV.") + if corr["uncertainty"] != 0 or corr["value"] == 0: + print(f"This correction has an uncertainty value of {corr['uncertainty']:f} eV.") + else: + print("This correction does not have uncertainty data available") + print("-" * 30) + + print(f"The final energy after corrections is {dct['corrected_energy']:f}") + + +@cached_class +class MaterialsProject2020Compatibility(Compatibility): + """This class implements the Materials Project 2020 energy correction scheme, which + incorporates uncertainty quantification and allows for mixing of GGA and GGA+U entries + (see References). + + Note that this scheme should only be applied to VASP calculations that use the + Materials Project input set parameters (see pymatgen.io.vasp.sets.MPRelaxSet). Using + this compatibility scheme on calculations with different parameters is not valid. + + The option `strict_anions` was added due to a bug. See PR #3803 (May 2024) for + related discussion. This behavior may change in subsequent versions as a more comprehensive + fix for this issue may be found. + + Note: While the correction scheme is largely composition-based, the energy corrections + applied to ComputedEntry and ComputedStructureEntry can differ for O and S-containing + structures if entry.data['oxidation_states'] is not populated or explicitly set. This + occurs because pymatgen will use atomic distances to classify O and S anions as + superoxide/peroxide/oxide and sulfide/polysulfide, resp. when oxidation states are not + provided. If you want the most accurate corrections possible, supply pre-defined + oxidation states to entry.data or pass ComputedStructureEntry. + """ + + def __init__( + self, + compat_type: str = "Advanced", + correct_peroxide: bool = True, + strict_anions: Literal["require_exact", "require_bound", "no_check"] = "require_bound", + check_potcar: bool = True, + check_potcar_hash: bool = False, + config_file: str | None = None, + ) -> None: + """ + Args: + compat_type: Two options, GGA or Advanced. GGA means all GGA+U + entries are excluded. Advanced means the GGA/GGA+U mixing scheme + of Jain et al. (see References) is implemented. In this case, + entries which are supposed to be calculated in GGA+U (i.e., + transition metal oxides and fluorides) will have the corresponding + GGA entries excluded. For example, Fe oxides should + have a U value under the Advanced scheme. An Fe oxide run in GGA + will therefore be excluded. + + To use the "Advanced" type, Entry.parameters must contain a "hubbards" + key which is a dict of all non-zero Hubbard U values used in the + calculation. For example, if you ran a Fe2O3 calculation with + Materials Project parameters, this would look like + entry.parameters["hubbards"] = {"Fe": 5.3}. If the "hubbards" key + is missing, a GGA run is assumed. Entries obtained from the + MaterialsProject database will automatically have these fields + populated. Default: "Advanced" + correct_peroxide: Specify whether peroxide/superoxide/ozonide + corrections are to be applied or not. If false, all oxygen-containing + compounds are assigned the 'oxide' correction. Default: True + strict_anions: only apply the anion corrections to anions. The option + "require_exact" will only apply anion corrections in cases where the + anion oxidation state is between the oxidation states used + in the experimental fitting data. The option "require_bound" will + define an anion as any species with an oxidation state value of <= -1. + This prevents the anion correction from being applied to unrealistic + hypothetical structures containing large proportions of very electronegative + elements, thus artificially over-stabilizing the compound. Set to "no_check" + to restore the original behavior described in the associated publication. Default: True + check_potcar (bool): Check that the POTCARs used in the calculation are consistent + with the Materials Project parameters. False bypasses this check altogether. Default: True + Can also be disabled globally by running `pmg config --add PMG_POTCAR_CHECKS false`. + check_potcar_hash (bool): Use potcar hash to verify POTCAR settings are + consistent with MPRelaxSet. If False, only the POTCAR symbols will + be used. Default: False + config_file (Path): Path to the selected compatibility.yaml config file. + If None, defaults to `MP2020Compatibility.yaml` distributed with + pymatgen. + + References: + Wang, A., Kingsbury, R., McDermott, M., Horton, M., Jain. A., Ong, S.P., + Dwaraknath, S., Persson, K. A framework for quantifying uncertainty + in DFT energy corrections. Scientific Reports 11: 15496, 2021. + https://doi.org/10.1038/s41598-021-94550-5 + + Jain, A. et al. Formation enthalpies by mixing GGA and GGA + U calculations. + Phys. Rev. B - Condens. Matter Mater. Phys. 84, 1-10 (2011). # codespell:ignore=Mater + """ + if compat_type not in ["GGA", "Advanced"]: + raise CompatibilityError(f"Invalid {compat_type=}") + + self.compat_type = compat_type + self.correct_peroxide = correct_peroxide + self.strict_anions = strict_anions + self.check_potcar = check_potcar + self.check_potcar_hash = check_potcar_hash + + # load corrections and uncertainties + if config_file: + if os.path.isfile(config_file): + self.config_file: str | None = config_file + config = loadfn(self.config_file) + else: + raise ValueError(f"Custom MaterialsProject2020Compatibility {config_file=} does not exist.") + else: + self.config_file = None + + config = MP2020_COMPAT_CONFIG + + self.name = config["Name"] + self.comp_correction = config["Corrections"].get("CompositionCorrections", defaultdict(float)) + self.comp_errors = config["Uncertainties"].get("CompositionCorrections", defaultdict(float)) + + if self.compat_type == "Advanced": + self.u_settings = MPRelaxSet.CONFIG["INCAR"]["LDAUU"] + self.u_corrections = config["Corrections"].get("GGAUMixingCorrections", defaultdict(float)) + self.u_errors = config["Uncertainties"].get("GGAUMixingCorrections", defaultdict(float)) + else: + self.u_settings = {} + self.u_corrections = {} + self.u_errors = {} + + def get_adjustments(self, entry: AnyComputedEntry) -> list[EnergyAdjustment]: + """Get the energy adjustments for a ComputedEntry or ComputedStructureEntry. + + Energy corrections are implemented directly in this method instead of in + separate AnionCorrection, GasCorrection, or UCorrection classes which + were used in the legacy correction scheme. + + Args: + entry: A ComputedEntry or ComputedStructureEntry object. + + Returns: + list[EnergyAdjustment]: A list of EnergyAdjustment to be applied to the Entry. + + Raises: + CompatibilityError if the entry is not compatible + """ + if entry.parameters.get("run_type") not in ("GGA", "GGA+U"): + raise CompatibilityError( + f"Entry {entry.entry_id} has invalid run type {entry.parameters.get('run_type')}. " + f"Must be GGA or GGA+U. Discarding." + ) + + # check the POTCAR symbols + # this should return ufloat(0, 0) or raise a CompatibilityError or ValueError + if entry.parameters.get("software", "vasp") == "vasp": + pc = PotcarCorrection( + MPRelaxSet, + check_hash=self.check_potcar_hash, + check_potcar=self.check_potcar, + ) + pc.get_correction(entry) + + # apply energy adjustments + adjustments: list[CompositionEnergyAdjustment] = [] + + comp = entry.composition + rform = comp.reduced_formula + # sorted list of elements, ordered by electronegativity + sorted_elements = sorted((el for el in comp.elements if comp[el] > 0), key=lambda el: el.X) + + # Skip single elements + if len(comp) == 1: + return adjustments + + # Check for sulfide corrections + if Element("S") in comp: + sf_type = "sulfide" + if entry.data.get("sulfide_type"): + sf_type = entry.data["sulfide_type"] + elif hasattr(entry, "structure"): + sf_type = sulfide_type(entry.structure) + + # use the same correction for polysulfides and sulfides + if sf_type == "polysulfide": + sf_type = "sulfide" + + if sf_type == "sulfide": + adjustments.append( + CompositionEnergyAdjustment( + self.comp_correction["S"], + comp["S"], + uncertainty_per_atom=self.comp_errors["S"], + name="MP2020 anion correction (S)", + cls=self.as_dict(), + ) + ) + + # Check for oxide, peroxide, superoxide, and ozonide corrections. + if Element("O") in comp: + if self.correct_peroxide: + # determine the oxide_type + if entry.data.get("oxide_type"): + ox_type = entry.data["oxide_type"] + elif hasattr(entry, "structure"): + ox_type = oxide_type(entry.structure, 1.05) + else: + warnings.warn( + "No structure or oxide_type parameter present. Note that peroxide/superoxide corrections " + "are not as reliable and relies only on detection of special formulas, e.g. Li2O2.", + stacklevel=2, + ) + + common_peroxides = [ + "Li2O2", + "Na2O2", + "K2O2", + "Cs2O2", + "Rb2O2", + "BeO2", + "MgO2", + "CaO2", + "SrO2", + "BaO2", + ] + common_superoxides = ["LiO2", "NaO2", "KO2", "RbO2", "CsO2"] + ozonides = ["LiO3", "NaO3", "KO3", "NaO5"] + + if rform in common_peroxides: + ox_type = "peroxide" + elif rform in common_superoxides: + ox_type = "superoxide" + elif rform in ozonides: + ox_type = "ozonide" + else: + ox_type = "oxide" + else: + ox_type = "oxide" + + if ox_type == "hydroxide": + ox_type = "oxide" + + adjustments.append( + CompositionEnergyAdjustment( + self.comp_correction[ox_type], + comp["O"], + uncertainty_per_atom=self.comp_errors[ox_type], + name=f"MP2020 anion correction ({ox_type})", + cls=self.as_dict(), + ) + ) + + # Check for anion corrections + # only apply anion corrections if the element is an anion + # first check for a pre-populated oxidation states key + # the key is expected to comprise a dict corresponding to the first element output by + # Composition.oxi_state_guesses(), e.g. {'Al': 3.0, 'S': 2.0, 'O': -2.0} for 'Al2SO4' + if "oxidation_states" not in entry.data: + # try to guess the oxidation states from composition + # for performance reasons, fail if the composition is too large + try: + oxi_states = entry.composition.oxi_state_guesses(max_sites=-20) + except ValueError: + oxi_states = ({},) + + entry.data["oxidation_states"] = (oxi_states or ({},))[0] + + if entry.data["oxidation_states"] == {}: + warnings.warn( + f"Failed to guess oxidation states for Entry {entry.entry_id} " + f"({entry.reduced_formula}). Assigning anion correction to " + "only the most electronegative atom.", + stacklevel=2, + ) + + for anion in ("Br", "I", "Se", "Si", "Sb", "Te", "H", "N", "F", "Cl"): + if Element(anion) in comp and anion in self.comp_correction: + apply_correction = False + oxidation_state = entry.data["oxidation_states"].get(anion, 0) + # if the oxidation_states key is not populated, only apply the correction if the anion + # is the most electronegative element + if oxidation_state < 0: + apply_correction = True + if self.strict_anions == "require_bound" and oxidation_state > -1: + # This is not an anion. Noting that the rare case of a fractional + # oxidation state in range [-1, 0] might be considered an anionic. + # This could include suboxides or metal-rich pnictides, chalcogenides etc. + # However! these cases are not included in the experimental fitting data + # used for the correction scheme, and so there is no information for + # whether the corrections are appropriate in this instance, and likely + # may. + apply_correction = False + else: + most_electroneg = sorted_elements[-1].symbol + if anion == most_electroneg: + apply_correction = True + + if self.strict_anions == "require_exact": + apply_correction = False + if (oxi_range := MP2020_ANION_OXIDATION_STATE_RANGES.get(anion)) and ( + oxi_range[0] <= oxidation_state <= oxi_range[1] + ): + apply_correction = True + + if apply_correction: + adjustments.append( + CompositionEnergyAdjustment( + self.comp_correction[anion], + comp[anion], + uncertainty_per_atom=self.comp_errors[anion], + name=f"MP2020 anion correction ({anion})", + cls=self.as_dict(), + ) + ) + + # GGA / GGA+U mixing scheme corrections + calc_u = entry.parameters.get("hubbards") + calc_u = defaultdict(int) if calc_u is None else calc_u + most_electroneg = sorted_elements[-1].symbol + u_corrections = self.u_corrections.get(most_electroneg, defaultdict(float)) + u_settings = self.u_settings.get(most_electroneg, defaultdict(float)) + u_errors = self.u_errors.get(most_electroneg, defaultdict(float)) + + for el in comp.elements: + symbol = el.symbol + # Check for bad U values + expected_u = float(u_settings.get(symbol, 0)) + actual_u = float(calc_u.get(symbol, 0)) + if actual_u != expected_u: + raise CompatibilityError( + f"Invalid U value of {actual_u:.3} on {symbol}, expected {expected_u:.3} for {entry.as_dict()}" + ) + if symbol in u_corrections: + adjustments.append( + CompositionEnergyAdjustment( + u_corrections[symbol], + comp[el], + uncertainty_per_atom=u_errors[symbol], + name=f"MP2020 GGA/GGA+U mixing correction ({symbol})", + cls=self.as_dict(), + ) + ) + + return adjustments + + +@cached_class +@due.dcite( + Doi( + "10.1103/PhysRevB.85.235438", + "Pourbaix scheme to combine calculated and experimental data", + ) +) +class MaterialsProjectAqueousCompatibility(Compatibility): + """This class implements the Aqueous energy referencing scheme for constructing + Pourbaix diagrams from DFT energies, as described in Persson et al. + + This scheme applies various energy adjustments to convert DFT energies into + Gibbs free energies of formation at 298 K and to guarantee that the experimental + formation free energy of H2O is reproduced. Briefly, the steps are: + + 1. Beginning with the DFT energy of O2, adjust the energy of H2 so that + the experimental reaction energy of -2.458 eV/H2O is reproduced. + 2. Add entropy to the DFT energy of any compounds that are liquid or + gaseous at room temperature + 3. Adjust the DFT energies of solid hydrate compounds (compounds that + contain water, e.g. FeO.nH2O) such that the energies of the embedded + H2O molecules are equal to the experimental free energy + + The above energy adjustments are computed dynamically based on the input + Entries. + + References: + K.A. Persson, B. Waldwick, P. Lazic, G. Ceder, Prediction of solid-aqueous + equilibria: Scheme to combine first-principles calculations of solids with + experimental aqueous states, Phys. Rev. B - Condens. Matter Mater. Phys. # codespell:ignore=Mater + 85 (2012) 1-12. doi:10.1103/PhysRevB.85.235438. + """ + + def __init__( + self, + solid_compat: Compatibility | type[Compatibility] | None = MaterialsProject2020Compatibility, + o2_energy: float | None = None, + h2o_energy: float | None = None, + h2o_adjustments: float | None = None, + universal_solid_shift_eV_per_atom: float = 0.0, # <--- NEW (total eV per entry) + apply_universal_shift_to: str = "compounds", + ) -> None: + """Initialize the MaterialsProjectAqueousCompatibility class. + + Note that this class requires as inputs the ground-state DFT energies of O2 and H2O, plus the value of any + energy adjustments applied to an H2O molecule. If these parameters are not provided in __init__, they can + be automatically populated by including ComputedEntry for the ground state of O2 and H2O in a list of + entries passed to process_entries. process_entries will fail if one or the other is not provided. + + Args: + solid_compat: Compatibility scheme used to pre-process solid DFT energies prior to applying aqueous + energy adjustments. May be passed as a class (e.g. MaterialsProject2020Compatibility) or an instance + (e.g., MaterialsProject2020Compatibility()). If None, solid DFT energies are used as-is. + Default: MaterialsProject2020Compatibility + o2_energy: The ground-state DFT energy of oxygen gas, including any adjustments or corrections, in eV/atom. + If not set, this value will be determined from any O2 entries passed to process_entries. + Default: None + h2o_energy: The ground-state DFT energy of water, including any adjustments or corrections, in eV/atom. + If not set, this value will be determined from any H2O entries passed to process_entries. + Default: None + h2o_adjustments: Total energy adjustments applied to one water molecule, in eV/atom. + If not set, this value will be determined from any H2O entries passed to process_entries. + Default: None + """ + self.solid_compat = None + # check whether solid_compat has been instantiated + if solid_compat is None: + self.solid_compat = None + elif isinstance(solid_compat, type) and issubclass(solid_compat, Compatibility): + self.solid_compat = solid_compat() + elif issubclass(type(solid_compat), Compatibility): + self.solid_compat = solid_compat + else: + raise ValueError("Expected a Compatibility class, instance of a Compatibility or None") + + self.o2_energy = o2_energy + self.h2o_energy = h2o_energy + self.h2_energy = None + self.h2o_adjustments = h2o_adjustments + + if not all([self.o2_energy, self.h2o_energy, self.h2o_adjustments]): + warnings.warn( + f"You did not provide the required O2 and H2O energies. {type(self).__name__} " + "needs these energies in order to compute the appropriate energy adjustments. It will try " + "to determine the values from ComputedEntry for O2 and H2O passed to process_entries, but " + "will fail if these entries are not provided.", + stacklevel=2, + ) + + # Standard state entropy of molecular-like compounds at 298K (-T delta S) + # from Kubaschewski Tables (eV/atom) + self.cpd_entropies = { + # exp anion entropy + "O2": 0.316731, + "N2": 0.295729, + "F2": 0.313025, + "Cl2": 0.344373, + "Br": 0.235039, + "Hg": 0.234421, + "H2O": 0.071963, # 0.215891 eV/H2O + # exp cation entropy + "C": 0.017737, + "S": 0.098265, + "Na": 0.158245, + "K": 0.181694, + "Ca": 0.127993, + "Mg": 0.100985, + "Li": 0.089984, + "P": 0.126972, + "Al": 0.087543, + # exp solid entropy + # oxides + "Na2O": 0.0773147, + "MgO": 0.04312, # microcrystal #0.0416239 (macrocrystal) + "CaO": 0.0614161, + "KO2": 0.1202056, + "K2O2": 0.0788753, + "Na2O2": 0.0733903, + "Li2O": 0.038699, + "Fe3O4": 0.06463, + "Fe2O3": 0.05402, + "Ca2Fe2O5": 0.06482, + "Mg(FeO2)2": 0.05465, + "Al2O3": 0.031470, + "CaAl2O4": 0.050422, + "CaAl4O7": 0.0457904, + "LiAlO2": 0.041211, + "LiAl5O8": 0.033062, + "SiO2": 0.043097, + "Al2FeO4": 0.04693, + "MgFe2O4": 0.05465, + # chlorides + "NaCl": 0.111445, + "KCl": 0.127606, + "H4NCl": 0.048721, + "MgCl2": 0.092312, + "CaCl2": 0.107742, + "LiCl": 0.0916683, + "FeCl2": 0.0789525, + "FeCl3": 0.074665, + "MgH2Cl2O": 0.0706607, # hydrate + "MgH4(ClO)2": 0.061768, # hydrate + "MgH8(ClO2)2": 0.0543861, # hydrate + "MgH12(ClO3)2": 0.0538711, # hydrate + "LiAl2H6ClO6": 0.039220162, # hydrate, #0.071213, #quacc 406.14803248222813 J/mol.K + # carbonates + "Li2CO3": 0.046542, + "NaHCO3": 0.052377, + "Na2CO3": 0.069517, + "K2CO3": 0.080096, + "CaCO3": 0.057414, + "MgCO3": 0.040604, + "CaMg(CO3)2": 0.047952, + "KHCO3": 0.059485, + "FeCO3": 0.057414, + "Na3H5(CO4)2": 0.051708, + "Na2H20CO13": 0.0483, + "Na2H2CO4": 0.05772, + "Na2H10SO5": 0.04944, + # sulfides + "MgS": 0.0777628, + "CaS": 0.087296, + "Na2S": 0.086214, + "K2S": 0.108154, + "FeS2": 0.0545, + "FeS": 0.09315, + "Na2S2O7": 0.056774, # hydrate + "K2S2O7": 0.071691, # hydrate + # "Na2S5": None, + # sulfates + "CaSO4": 0.054953, + "MgSO4": 0.047176, + "K2SO4": 0.077500, + "Na2SO4": 0.06603, + "KHSO4": 0.060964, + "NaHSO4": 0.049883, + "Li2SO4": 0.0508103, + "FeSO4": 0.05536, + "Al2(SO4)3": 0.043498, + "CaH4SO6": 0.049983, # hydrate + "Ca2H2S2O9": 0.053768, # hydrate + "MgH12SO10": 0.0448196, # hydrate + "MgH12SO9": 0.04328852, # hydrate + "MgH14SO11": 0.042575, # hydrate + "MgH2SO5": 0.0433990, # hydrate + "FeH14SO11": 0.0468325, # hydrate + # nitrates + "Ca(NO3)2": 0.066369, + "NaNO3": 0.072012, + "NaNO2": 0.080189, + "Mg(NO3)2": 0.056309, + "KNO3": 0.082228, + "KNO2": 0.117494, + "MgPH16NO10": 0.03462, # hydrate #0.04192 #struvite + "MgH12(NO6)2": 0.0517309, # hydrate + # phosphates + "P2O5": 0.050515, + "FePH2O5": 0.044098, # hydrate + "Ca2P2O7": 0.05316, + "FeP(H2O3)2": 0.044098, # hydrate + "Mg2P2O7": 0.04351, + "Mg3(PO4)2": 0.044973, + "Ca3(PO4)2": 0.056098, + "CaPHO4": 0.0491682, + "CaPH5O6": 0.0450325, # hydrate + "CaP2(H2O3)3": 0.044601, # hydrate + "Ca5P3HO13": 0.0574393, # hydrate + # nitrogen compounds + "NaN3": 0.074827, + # carbon compounds + "CaC2": 0.072062, + "Fe3C": 0.07177, + # hydroxides + "KHO": 0.081270, + "NaHO": 0.066391, + "Ca(HO)2": 0.051537, + "Mg(HO)2": 0.039047, + "LiHO": 0.044086, + "Li2O2": 0.037785, + "FeHO2": 0.04664, + "LiH3O2": 0.0366745, # hydrate + "Al(HO)3": 0.0302169, + "AlHO2": 0.0374136, # 0.0272973 polymorph + # hydrides + "NaH": 0.0618271, + "NaH2N": 0.0594075, + "LiH": 0.0309135, + "CaH2": 0.043262, + "LiAlH4": 0.0405526, + "Li3AlH6": 0.0317015, + # cyanates + "NaCNO": 0.0747035, + "KCSN": 0.0959945, + # others + "CaMg2": 0.1074022, + "H4CN2O": 0.0461752, + # silicate + "Mg3Si2H4O9": 0.038, + "MgSiO3": 0.041865, + "CaMg(SiO3)2": 0.044167, + "Ca2Mg5Si8(HO12)2": 0.04137, + "Mg3Si4(HO6)2": 0.038362, + "NaAlSi3O8": 0.0493, + # "CaAl2(SiO4)2", + # "NaAlSi2H2O7", + # "KAl3Si3(HO6)2", + # "Al2Si2H4O9", + } + self.name = "MP Aqueous free energy adjustment" + super().__init__() + + self.universal_solid_shift_eV_per_atom = universal_solid_shift_eV_per_atom + self.apply_universal_shift_to = apply_universal_shift_to + + def get_adjustments(self, entry: ComputedEntry) -> list[EnergyAdjustment]: + """Get the corrections applied to a particular entry. + + Args: + entry: A ComputedEntry object. + + Returns: + list[EnergyAdjustment]: Energy adjustments to be applied to entry. + + Raises: + CompatibilityError if the required O2 and H2O energies have not been provided to + MaterialsProjectAqueousCompatibility during init or in the list of entries passed to process_entries. + """ + adjustments = [] + if self.o2_energy is None or self.h2o_energy is None or self.h2o_adjustments is None: + raise CompatibilityError( + "You did not provide the required O2 and H2O energies. " + f"{type(self).__name__} needs these energies in order to compute " + "the appropriate energy adjustments. Either specify the energies as arguments " + f"to {type(self).__name__}.__init__ or run process_entries on a list that includes ComputedEntry for " + "the ground state of O2 and H2O." + ) + + # compute the free energies of H2 and H2O (eV/atom) to guarantee that the + # formation-free energy of H2O is equal to -2.4583 eV/H2O from experiments + # (MU_H2O from Pourbaix module) + + # Free energy of H2 in eV/atom, fitted using Eq. 40 of Persson et al. PRB 2012 85(23) + # https://journals.aps.org/prb/abstract/10.1103/PhysRevB.85.235438 + # for this calculation ONLY, we need the (corrected) DFT energy of water + self.fit_h2_energy = round( + 0.5 + * ( + 3 * (self.h2o_energy - self.cpd_entropies["H2O"]) - (self.o2_energy - self.cpd_entropies["O2"]) - MU_H2O + ), + 6, + ) + + comp = entry.composition + rform = comp.reduced_formula + + # use fit_h2_energy to adjust the energy of all H2 polymorphs such that + # the lowest energy polymorph has the correct experimental value + # if H2O and O2 energies have been set explicitly via kwargs, then + # all H2 polymorphs will get the same energy. + if rform == "H2": + if self.h2_energy is None: + raise ValueError("H2 energy not set") + adjustments.append( + ConstantEnergyAdjustment( + (self.fit_h2_energy - self.h2_energy) * comp.num_atoms, + uncertainty=np.nan, + name="MP Aqueous H2 / H2O referencing", + cls=self.as_dict(), + description="Adjusts the H2 energy to reproduce the experimental " + "Gibbs formation free energy of H2O, based on the DFT energy " + "of Oxygen and H2O", + ) + ) + + # add minus T delta S to the DFT energy (enthalpy) of compounds that are + # molecular-like at room temperature + if rform in self.cpd_entropies: + adjustments.append( + TemperatureEnergyAdjustment( + -self.cpd_entropies[rform] / 298, + 298, + comp.num_atoms, + uncertainty_per_deg=np.nan, + name="Compound entropy at room temperature", + cls=self.as_dict(), + description="Adds the entropy (T delta S) to energies of compounds that " + "are gaseous or liquid at standard state", + ) + ) + + MU_N_CORRECTION = 0.26 + # For nitrogen compounds, we apply a correction to the DFT energy + if rform != "N2" and "N" in comp: + n_N = comp["N"] + n_correction = MU_N_CORRECTION # * n_N + + adjustments.append( + CompositionEnergyAdjustment( + n_correction, + n_N, + uncertainty_per_atom=np.nan, + name="MP Aqueous Nitrogen correction", + cls=self.as_dict(), + description="Adjust the energy of solid nitrogen compounds so that the" + "free energies match the experimental" + " value enforced by the MP Aqueous energy referencing scheme.", + ) + ) + + # Universal correction for solid/compounds + if self.universal_solid_shift_eV_per_atom: + is_element = comp.is_element + + molecular_like_rforms = {"O2", "N2", "F2", "Cl2", "Br", "Hg"} + is_molecular_standard_state = rform in molecular_like_rforms + is_special_ref = rform in {"H2", "H2O", "O2"} + + if self.apply_universal_shift_to == "compounds": + apply_shift = (not is_element) and (not is_molecular_standard_state) and (not is_special_ref) + elif self.apply_universal_shift_to == "all_solids": + apply_shift = (not is_element) and (not is_special_ref) + else: + raise ValueError("apply_universal_shift_to must be one of: 'compounds', 'all_solids'") + + if apply_shift: + total_shift = self.universal_solid_shift_eV_per_atom * comp.num_atoms + + adjustments.append( + ConstantEnergyAdjustment( + total_shift, + uncertainty=np.nan, + name="User universal solid shift (eV/atom)", + cls=self.as_dict(), + description=( + f"Applies a user-defined shift of {self.universal_solid_shift_eV_per_atom:+.4f} eV/atom " + f"({total_shift:+.4f} eV total) to selected entries." + ), + ) + ) + + # # TODO - detection of embedded water molecules is not very sophisticated + # # Should be replaced with some kind of actual structure detection + + # # For any compound except water, check if it is a hydrate (contains + # # H2O in its structure). If so, adjust the energy to remove MU_H2O eV per + # # embedded water molecule. + # # in other words, we assume that the DFT energy of such a compound is really + # # a superposition of the "real" solid DFT energy (FeO in this case) and the free + # # energy of some water molecules + # # e.g. that E_FeO.nH2O = E_FeO + n * g_H2O + # # so, to get the most accurate Gibbs free energy, we want to replace + # # g_FeO.nH2O = E_FeO.nH2O + dE_Fe + (n+1) * dE_O + 2n dE_H + # # with + # # g_FeO = E_FeO.nH2O + dE_Fe + dE_O + n g_H2O + # # where E is DFT energy, dE is an energy correction, and g is Gibbs free energy + # # of formation + # # This means we have to 1) reverse any energy corrections that have already been + # # applied to H and O in water and then 2) remove the free energy of the water + # # molecules from the hydrated solid energy. + # if rform != "H2O": + # # count the number of whole water molecules in the composition + # rcomp, factor = comp.get_reduced_composition_and_factor() + # nH2O = int(min(rcomp["H"] / 2.0, rcomp["O"])) * factor + # if nH2O > 0: + # # first, remove any H or O corrections already applied to H2O in the + # # formation energy so that we don't double count them + # # next, remove MU_H2O for each water molecule present + # hydrate_adjustment = -1 * (self.h2o_adjustments * 3 + MU_H2O) + + # adjustments.append( + # CompositionEnergyAdjustment( + # hydrate_adjustment, + # nH2O, + # uncertainty_per_atom=np.nan, + # name="MP Aqueous hydrate", + # cls=self.as_dict(), + # description="Adjust the energy of solid hydrate compounds (compounds " + # "containing H2O molecules in their structure) so that the " + # "free energies of embedded H2O molecules match the experimental" + # " value enforced by the MP Aqueous energy referencing scheme.", + # ) + # ) + + return adjustments + + def process_entries( + self, + entries: list[AnyComputedEntry], + clean: bool = False, + verbose: bool = False, + inplace: bool = True, + n_workers: int = 1, + on_error: Literal["ignore", "warn", "raise"] = "ignore", + ) -> list[AnyComputedEntry]: + """Process a sequence of entries with the chosen Compatibility scheme. + + Args: + entries (list[ComputedEntry | ComputedStructureEntry]): Entries to be processed. + clean (bool): Whether to remove any previously-applied energy adjustments. + If True, all EnergyAdjustment are removed prior to processing the Entry. + Default is False. + verbose (bool): Whether to display progress bar for processing multiple entries. + Default is False. + inplace (bool): Whether to modify the entries in place. If False, a copy of the + entries is made and processed. Default is True. + n_workers (int): Number of workers to use for parallel processing. Default is 1. + on_error ('ignore' | 'warn' | 'raise'): What to do when get_adjustments(entry) + raises CompatibilityError. Defaults to 'ignore'. + + Returns: + list[AnyComputedEntry]: Adjusted entries. Entries in the original list incompatible with + chosen correction scheme are excluded from the returned list. + """ + # Convert input arg to a list if not already + if isinstance(entries, ComputedEntry): + entries = [entries] + + # If not inplace, process entries on a copy + if not inplace: + entries = copy.deepcopy(entries) + + # Pre-process entries with the given solid compatibility class + if self.solid_compat: + entries = self.solid_compat.process_entries(entries, clean=True, inplace=inplace, n_workers=n_workers) + + # when processing single entries, all H2 polymorphs will get assigned the + # same energy + if len(entries) == 1 and entries[0].reduced_formula == "H2": + warnings.warn( + "Processing single H2 entries will result in the all polymorphs " + "being assigned the same energy. This should not cause problems " + "with Pourbaix diagram construction, but may be confusing. " + "Pass all entries to process_entries() at once in if you want to " + "preserve H2 polymorph energy differences.", + stacklevel=2, + ) + + # extract the DFT energies of oxygen and water from the list of entries, if present + # do not do this when processing a single entry, as it might lead to unintended + # results + if len(entries) > 1: + if not self.o2_energy and (o2_entries := [e for e in entries if e.reduced_formula == "O2"]): + self.o2_energy = min(e.energy_per_atom for e in o2_entries) + + if not self.h2o_energy and not self.h2o_adjustments: # noqa: SIM102 + if h2o_entries := [e for e in entries if e.reduced_formula == "H2O"]: + h2o_entries = sorted(h2o_entries, key=lambda e: e.energy_per_atom) + self.h2o_energy = h2o_entries[0].energy_per_atom + self.h2o_adjustments = h2o_entries[0].correction / h2o_entries[0].composition.num_atoms + + if h2_entries := [e for e in entries if e.reduced_formula == "H2"]: + h2_entries = sorted(h2_entries, key=lambda e: e.energy_per_atom) + self.h2_energy = h2_entries[0].energy_per_atom # type: ignore[assignment] + + return super().process_entries( + entries, + clean=clean, + verbose=verbose, + inplace=inplace, + n_workers=n_workers, + on_error=on_error, + ) + + +def needs_u_correction( + comp: CompositionLike, + u_config: dict[str, dict[str, float]] = MP2020_COMPAT_CONFIG["Corrections"]["GGAUMixingCorrections"], +) -> set[str]: + """Check if a composition is Hubbard U-corrected in the Materials Project 2020 + GGA/GGA+U mixing scheme. + + Args: + comp (CompositionLike): The formula/composition to check. + u_config (dict): The U-correction configuration to use. Default is the + Materials Project 2020 configuration. + + Returns: + set[str]: The subset of elements whose combination requires a U-correction. Pass + return value to bool(ret_val) if you just want True/False. + """ + elements = set(map(str, Composition(comp).elements)) + has_u_anion = set(u_config) & elements + + u_corrected_cations = set(u_config["O"]) + has_u_cation = u_corrected_cations & elements + if has_u_cation and has_u_anion: + return has_u_cation | has_u_anion + return set() diff --git a/src/pyEQL/pourbaix/ion.py b/src/pyEQL/pourbaix/ion.py new file mode 100644 index 00000000..66c2f3db --- /dev/null +++ b/src/pyEQL/pourbaix/ion.py @@ -0,0 +1,348 @@ +"""Module containing class to create an ion.""" + +from __future__ import annotations + +import re +from copy import deepcopy +from typing import TYPE_CHECKING + +from monty.json import MSONable +from pymatgen.core.composition import Composition, reduce_formula +from pymatgen.util.string import Stringify, charge_string, formula_double_format + +if TYPE_CHECKING: + from typing_extensions import Self + + +class Ion(Composition, MSONable, Stringify): + """Just a Composition object with an additional variable to store charge. + + The net charge can either be represented as Mn++, Mn+2, Mn[2+], Mn[++], or + Mn[+2]. Note the order of the sign and magnitude in each representation. + """ + + def __init__(self, composition: Composition, charge: float = 0.0, **kwargs) -> None: + """Flexible Ion construction, similar to Composition. + For more information, please see pymatgen.core.Composition. + """ + super().__init__(composition, **kwargs) + self._charge = charge + + def __eq__(self, other: object) -> bool: + if not isinstance(other, type(self)): + return NotImplemented + if self.composition != other.composition: + return False + return self.charge == other.charge + + def __add__(self, other) -> Self: + """Addition of two ions.""" + new_composition = self.composition + other.composition + new_charge = self.charge + other.charge + return type(self)(new_composition, new_charge) + + def __sub__(self, other) -> Self: + """Subtraction of two ions.""" + new_composition = self.composition - other.composition + new_charge = self.charge - other.charge + return type(self)(new_composition, new_charge) + + def __mul__(self, other) -> Self: + """Multiplication of an Ion with a factor.""" + new_composition = self.composition * other + new_charge = self.charge * other + return type(self)(new_composition, new_charge) + + def __hash__(self) -> int: + return hash((self.composition, self.charge)) + + def __str__(self) -> str: + return self.formula + + def __repr__(self) -> str: + return f"Ion: {self.formula}" + + @classmethod + def from_formula(cls, formula: str) -> Self: + """Create Ion from formula. The net charge can either be represented as + Mn++, Mn+2, Mn[2+], Mn[++], or Mn[+2]. Note the order of the sign and + magnitude in each representation. + + Also note that (aq) can be included in the formula, e.g. "NaOH (aq)". + + Args: + formula (str): The formula to create ion from. + + Returns: + Ion + """ + # Strip (aq), if present + if match := re.search(r"\(aq\)", formula): + formula = formula.replace(match.group(), "", 1) + + # Check for charge in brackets + charge = 0.0 + if match := re.search(r"\[([^\[\]]+)\]", formula): + if m_chg := re.search(r"([\.\d]*)([+-]*)([\.\d]*)", match[1]): + if m_chg[1] != "": + if m_chg[3] != "": + raise ValueError("Invalid formula") + charge += float(m_chg[1]) * (float(f"{m_chg[2]}1")) + elif m_chg[3] != "": + charge += float(m_chg[3]) * (float(f"{m_chg[2]}1")) + else: + for val in re.findall("[+-]", m_chg[2]): + charge += float(f"{val}1") + + formula = formula.replace(match.group(), "", 1) + + # If no brackets, parse trailing +/- + for m_chg in re.finditer(r"([+-])([\.\d]*)", formula): + sgn = float(f"{m_chg[1]}1") + charge += float(m_chg[2]) * sgn if m_chg[2].strip() != "" else sgn + formula = formula.replace(m_chg.group(), "", 1) + + return cls(Composition(formula), charge) + + @property + def formula(self) -> str: + """A formula string with appended charge. The + charge is written with the sign preceding the magnitude, e.g. + 'Ca1 +2'. Uncharged species have "(aq)" appended, e.g. "O2 (aq)". + """ + formula = super().formula + return f"{formula} {charge_string(self.charge, brackets=False)}" + + @property + def anonymized_formula(self) -> str: + """An anonymized formula. Appends charge to the end + of anonymized composition. + """ + anon_formula = super().anonymized_formula + chg_str = charge_string(self._charge, brackets=False) + return anon_formula + chg_str + + def get_reduced_formula_and_factor( + self, + iupac_ordering: bool = False, + hydrates: bool = False, + ) -> tuple[str, float]: + """Calculate a reduced formula and factor. + + Similar to Composition.get_reduced_formula_and_factor except that O-H formulas + receive special handling to differentiate between hydrogen peroxide and OH-. + Formulas containing HO are written with oxygen first (e.g. 'Fe(OH)2' rather than + 'Fe(HO)2'), and special formulas that apply to solids (e.g. Li2O2 instead of LiO) + are not used. + + Note that the formula returned by this method does not contain a charge. + To include charge, use formula or reduced_formula instead. + + Args: + iupac_ordering (bool, optional): Whether to order the + formula by the IUPAC "electronegativity" series, defined in + Table VI of "Nomenclature of Inorganic Chemistry (IUPAC + Recommendations 2005)". This ordering effectively follows + the groups and rows of the periodic table, except the + Lanthanides, Actinides and hydrogen. Note that polyanions + will still be determined based on the true electronegativity of + the elements. + hydrates: If True (default), attempt to recognize hydrated metal + complexes and separate out the H2O in the reduced formula. + For example, Zr(OH)4 becomes ZrO2.2H2O. Applies only to + Ions containing metals. + + Returns: + tuple[str, float]: A pretty normalized formula and a multiplicative factor, i.e., + H4O4 returns ('H2O2', 2.0). + """ + all_int = all(abs(val - round(val)) < Composition.amount_tolerance for val in self.values()) + if not all_int: + return self.formula.replace(" ", ""), 1 + + comp = self.composition + nH2O = 0 + if hydrates: + # Detect hydrated metal complexes + nH = comp.get("H", 0) + nO = comp.get("O", 0) + if nO > 0 and any(e.is_metal for e in comp): + nH2O = int(nO) if nH >= 2 * nO else int(nH) // 2 + comp = self.composition - nH2O * Composition("H2O") + + el_amt_dict = {k: round(v) for k, v in comp.get_el_amt_dict().items()} + formula, factor = reduce_formula(el_amt_dict, iupac_ordering=iupac_ordering) + + # This line checks specifically that the contains an equal amount of O and H. When that is the case, + # they should be displayed as "OH" rather than "HO". + if self.composition.get("H") == self.composition.get("O"): + formula = formula.replace("HO", "OH") + if nH2O > 0: + formula += f".{nH2O}H2O" + + # Special handling of peroxide, acetic acid / acetate, and small alcohols + if formula == "OH" and self.charge == 0: + formula = "H2O2" + factor /= 2 + # acetic acid + elif formula == "H2CO": + formula = "CH3COOH" + factor /= 2 + # phosphoric acid system + elif formula == "PH3O4": + formula = "H3PO4" + elif formula == "PHO4": + formula = "HPO4" + elif formula == "P(HO2)2": + formula = "H2PO4" + # acetate + elif formula == "H3(CO)2": + formula = "CH3COO" + # methanol + elif formula == "H4CO": + formula = "CH3OH" + # ethanol + elif formula == "H6C2O": + formula = "C2H5OH" + # propanol + elif formula == "H8C3O": + formula = "C3H7OH" + # butanol + elif formula == "H10C4O": + formula = "C4H9OH" + elif formula == "O" and factor % 3 == 0: + formula = "O3" + factor /= 3 + # ammonia + elif formula == "H4N": + formula = "NH4" + elif formula == "H3N": + formula = "NH3" + # methane + elif formula == "H4C": + formula = "CH4" + # thiocyanate + elif formula == "CSN": + formula = "SCN" + # triiodide, nitride, an phosphide + elif (formula in ["N", "P"] and self.charge == -1) or (formula == "I" and self.charge == 1 / 3): + formula += "3" + factor /= 3 + # formate # codespell:ignore + elif formula == "HCOO": + formula = "HCO2" + # oxalate + elif formula == "CO2" and self.charge == -2: + formula = "C2O4" + factor /= 2 + # diatomic gases + elif formula in {"O", "N", "F", "Cl", "H"} and factor % 2 == 0: + formula += "2" + factor /= 2 + + return formula, factor + + @property + def reduced_formula(self) -> str: + """A reduced formula string with appended charge. The + charge is placed in brackets with the sign preceding the magnitude, e.g. + 'Ca[+2]'. Uncharged species have "(aq)" appended, e.g. "O2(aq)". + """ + formula, factor = self.get_reduced_formula_and_factor() + charge = self._charge / factor + chg_str = charge_string(charge) + return formula + chg_str + + @property + def alphabetical_formula(self) -> str: + """A formula string, with elements sorted by alphabetically and + appended charge. + """ + alph_formula = self.composition.alphabetical_formula + return f"{alph_formula} {charge_string(self.charge, brackets=False)}" + + @property + def charge(self) -> float: + """Charge of the ion.""" + return self._charge + + def as_dict(self) -> dict[str, float]: + """ + Returns: + dict with composition, as well as charge. + """ + dct = super().as_dict() + dct["charge"] = self.charge + return dct + + @classmethod + def from_dict(cls, dct: dict) -> Self: + """Generate an ion object from a dict created by as_dict(). + + Args: + dct: {symbol: amount} dict. + """ + dct_copy = deepcopy(dct) + charge = dct_copy.pop("charge") + composition = Composition(dct_copy) + return cls(composition, charge) + + @property + def to_reduced_dict(self) -> dict: + """ + Returns: + dict with element symbol and reduced amount e.g. + {"Fe": 2.0, "O":3.0}. + """ + dct = self.composition.to_reduced_dict + dct["charge"] = self.charge + return dct + + @property + def composition(self) -> Composition: + """Composition of ion.""" + return Composition(self._data) + + def oxi_state_guesses( + self, + oxi_states_override: dict | None = None, + all_oxi_states: bool = False, + max_sites: int | None = None, + ) -> list[dict[str, float]]: + """Check if the composition is charge-balanced and returns all + charge-balanced oxidation state combinations. Composition must have + integer values. Note that more num_atoms in the composition gives + more degrees of freedom. e.g. if possible oxidation states of + element X are [2,4] and Y are [-3], then XY is not charge balanced + but X2Y2 is. Results are returned from most to least probable based + on ICSD statistics. Use max_sites to improve performance if needed. + + Args: + oxi_states_override (dict): dict of str->list to override an + element's common oxidation states, e.g. {"V": [2,3,4,5]} + all_oxi_states (bool): if True, an element defaults to + all oxidation states in pymatgen Element.icsd_oxidation_states. + Otherwise, default is Element.common_oxidation_states. Note + that the full oxidation state list is *very* inclusive and + can produce nonsensical results. + max_sites (int): if possible, will reduce Compositions to at most + this many sites to speed up oxidation state guesses. If the + composition cannot be reduced to this many sites a ValueError + will be raised. Set to -1 to just reduce fully. If set to a + number less than -1, the formula will be fully reduced but a + ValueError will be thrown if the number of atoms in the reduced + formula is greater than abs(max_sites). + + Returns: + A list of dicts - each dict reports an element symbol and average + oxidation state across all sites in that composition. If the + composition is not charge balanced, an empty list is returned. + """ + return self._get_oxi_state_guesses(all_oxi_states, max_sites, oxi_states_override, self.charge)[0] # type: ignore[return-value] + + def to_pretty_string(self) -> str: + """Pretty string with proper superscripts.""" + _str = super().reduced_formula + if val := formula_double_format(self.charge, ignore_ones=False): + _str += f"^{val:+}" + return _str diff --git a/src/pyEQL/pourbaix/mpr_reference_ion_database.json b/src/pyEQL/pourbaix/mpr_reference_ion_database.json new file mode 100644 index 00000000..70eaa593 --- /dev/null +++ b/src/pyEQL/pourbaix/mpr_reference_ion_database.json @@ -0,0 +1,9641 @@ +[ + { + "data": { + "MajElements": "Be", + "RefSolid": "BeO", + "charge": { + "display": "2.0", + "error": null, + "unit": "", + "value": 2.0 + }, + "reference": "H. E. Barner and R. V. Scheuerman, Handbook of thermochemical data for compounds and aqueous species, Wiley, New York (1978)", + "\u0394G\u1da0": { + "display": "-379.86 kJ/mol", + "error": null, + "unit": "kJ/mol", + "value": -379.86 + }, + "\u0394G\u1da0RefSolid": { + "display": "-580.3 kJ/mol", + "error": null, + "unit": "kJ/mol", + "value": -580.3 + } + }, + "formula": "Be[+2]", + "identifier": "Be[2+]" + }, + { + "data": { + "MajElements": "Be", + "RefSolid": "BeO", + "charge": { + "display": "-2.0", + "error": null, + "unit": "", + "value": -2.0 + }, + "reference": "H. E. Barner and R. V. 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Sci. 39, 969-980 (1997)", + "\u0394G\u1da0": { + "display": "-586.57 kJ/mol", + "error": null, + "unit": "kJ/mol", + "value": -586.57 + }, + "\u0394G\u1da0RefSolid": { + "display": "-211.6 kJ/mol", + "error": null, + "unit": "kJ/mol", + "value": -211.6 + } + }, + "formula": "Ni(OH)3[-1]", + "identifier": "Ni(OH)3[-]" + }, + { + "data": { + "MajElements": "Ni", + "RefSolid": "NiO", + "charge": { + "display": "-1.0", + "error": null, + "unit": "", + "value": -1.0 + }, + "reference": "Shock, E.L., Sassani, D.C., Willis, M., and Sverjensky, D.A., 1997, Geochim. Cosmo. Acta, v. 61, no. 5, pp. 907-950", + "\u0394G\u1da0": { + "display": "-343.0 kJ/mol", + "error": null, + "unit": "kJ/mol", + "value": -343.0 + }, + "\u0394G\u1da0RefSolid": { + "display": "-211.6 kJ/mol", + "error": null, + "unit": "kJ/mol", + "value": -211.6 + } + }, + "formula": "NiHO2[-1]", + "identifier": "HNiO2[-]" + }, + { + "data": { + "MajElements": "Ni", + "RefSolid": "NiO", + "charge": { + "display": "0.0", + "error": null, + "unit": "", + "value": 0.0 + }, + "reference": "Shock, E.L., Sassani, D.C., Willis, M., and Sverjensky, D.A., 1997, Geochim. Cosmo. Acta, v. 61, no. 5, pp. 907-950", + "\u0394G\u1da0": { + "display": "-164.6 kJ/mol", + "error": null, + "unit": "kJ/mol", + "value": -164.6 + }, + "\u0394G\u1da0RefSolid": { + "display": "-211.6 kJ/mol", + "error": null, + "unit": "kJ/mol", + "value": -211.6 + } + }, + "formula": "NiO(aq)", + "identifier": "NiO(aq)" + }, + { + "data": { + "MajElements": "S", + "RefSolid": "Na2SO4", + "charge": { + "display": "0.0", + "error": null, + "unit": "", + "value": 0.0 + }, + "reference": "Shock, E.L., Sassani, D.C., Willis, M., and Sverjensky, D.A., 1997, Inorganic species in geologic fluids: Correlations among standard molal thermodynamic properties of aqueous ions and hydroxide complexes: Geochim. Cosmo. Acta, v. 61, no. 5, pp. 907-950.", + "\u0394G\u1da0": { + "display": "-535.54 kJ/mol", + "error": null, + "unit": "kJ/mol", + "value": -535.54 + }, + "\u0394G\u1da0RefSolid": { + "display": "-1,279.0 kJ/mol", + "error": null, + "unit": "kJ/mol", + "value": -1279.0 + } + }, + "formula": "H2S2O3(aq)", + "identifier": "H2S2O3(aq)" + }, + { + "data": { + "MajElements": "S", + "RefSolid": "Na2SO4", + "charge": { + "display": "-1.0", + "error": null, + "unit": "", + "value": -1.0 + }, + "reference": "Shock, E.L., Sassani, D.C., Willis, M., and Sverjensky, D.A., 1997, Inorganic species in geologic fluids: Correlations among standard molal thermodynamic properties of aqueous ions and hydroxide complexes: Geochim. Cosmo. Acta, v. 61, no. 5, pp. 907-950.", + "\u0394G\u1da0": { + "display": "-532.19 kJ/mol", + "error": null, + "unit": "kJ/mol", + "value": -532.19 + }, + "\u0394G\u1da0RefSolid": { + "display": "-1,279.0 kJ/mol", + "error": null, + "unit": "kJ/mol", + "value": -1279.0 + } + }, + "formula": "HS2O3[-1]", + "identifier": "HS2O3[-]" + } +] diff --git a/src/pyEQL/pourbaix/pourbaix_api.py b/src/pyEQL/pourbaix/pourbaix_api.py new file mode 100644 index 00000000..9a9b743b --- /dev/null +++ b/src/pyEQL/pourbaix/pourbaix_api.py @@ -0,0 +1,388 @@ +import itertools +import warnings +from importlib.resources import files +from typing import Literal + +import pandas as pd +from emmet.core.settings import EmmetSettings +from monty.serialization import loadfn +from mp_api.client.core.settings import MAPIClientSettings +from pymatgen.analysis.phase_diagram import PhaseDiagram +from pymatgen.analysis.pourbaix_diagram import Ion, IonEntry +from pymatgen.core import Composition, Element + +from pyEQL import Solution +from pyEQL.utils import standardize_formula + +_EMMET_SETTINGS = EmmetSettings() +_MAPI_SETTINGS = MAPIClientSettings() + + +class Pourbaix_api: + def __init__(self, mpr, comp_dict: dict | None = None): + ref_db_file = files("pyEQL") / "pourbaix" / "mpr_reference_ion_database.json" + ref_xlsx_file = files("pyEQL") / "pourbaix" / "NBS_Tables_Library.xlsx" + self.json_path = str(ref_db_file) + self.xlsx_path = str(ref_xlsx_file) + self.mpr = mpr + self.comp_dict = comp_dict + + # @classmethod + def get_ion_reference_data_for_chemsys(self, chemsys: str | list) -> list[dict]: + """Download aqueous ion reference data used in the construction of Pourbaix diagrams. + + Use this method to examine the ion reference data and to add additional + ions if desired. The data returned from this method can be passed to + get_ion_entries(). + + Data are retrieved from the Aqueous Ion Reference Data project + hosted on MPContribs. Refer to that project and its associated documentation + for more details about the format and meaning of the data. + + Args: + chemsys (str or [str]): Chemical system string comprising element + symbols separated by dashes, e.g., "Li-Fe-O" or List of element + symbols, e.g., ["Li", "Fe", "O"]. + + Returns: + [dict]: Among other data, each record contains 1) the experimental ion free energy, 2) the + formula of the reference solid for the ion, and 3) the experimental free energy of the + reference solid. All energies are given in kJ/mol. An example is given below. + + {'identifier': 'Li[+]', + 'formula': 'Li[+]', + 'data': {'charge': {'display': '1.0', 'value': 1.0, 'unit': ''}, + 'ΔGᶠ': {'display': '-293.71 kJ/mol', 'value': -293.71, 'unit': 'kJ/mol'}, + 'MajElements': 'Li', + 'RefSolid': 'Li2O', + 'ΔGᶠRefSolid': {'display': '-561.2 kJ/mol', + 'value': -561.2, + 'unit': 'kJ/mol'}, + 'reference': 'H. E. Barner and R. V. Scheuerman, Handbook of thermochemical data for + compounds and aqueous species, Wiley, New York (1978)'}} + """ + ion_data = loadfn(self.json_path) + + if isinstance(chemsys, str): + chemsys = chemsys.split("-") + return [d for d in ion_data if d["data"]["MajElements"] in chemsys] + + # @classmethod + def get_ion_entries(self, pd: PhaseDiagram, ion_ref_data: list[dict] | None = None) -> list[IonEntry]: + """Retrieve IonEntry objects that can be used in the construction of + Pourbaix Diagrams. The energies of the IonEntry are calculaterd from + the solid energies in the provided Phase Diagram to be + consistent with experimental free energies. + + NOTE! This is an advanced method that assumes detailed understanding + of how to construct computational Pourbaix Diagrams. If you just want + to build a Pourbaix Diagram using default settings, use get_pourbaix_entries. + + Args: + pd: Solid phase diagram on which to construct IonEntry. Note that this + Phase Diagram MUST include O and H in its chemical system. For example, + to retrieve IonEntry for Ti, the phase diagram passed here should contain + materials in the H-O-Ti chemical system. It is also assumed that solid + energies have already been corrected with MaterialsProjectAqueousCompatibility, + which is necessary for proper construction of Pourbaix diagrams. + ion_ref_data: Aqueous ion reference data. If None (default), the data + are downloaded from the Aqueous Ion Reference Data project hosted + on MPContribs. To add a custom ionic species, first download + data using get_ion_reference_data, then add or customize it with + your additional data, and pass the customized list here. + + Returns: + [IonEntry]: IonEntry are similar to PDEntry objects. Their energies + are free energies in eV. + """ + # determine the chemsys from the phase diagram + chemsys = "-".join([el.symbol for el in pd.elements]) + + # raise ValueError if O and H not in chemsys + if "O" not in chemsys or "H" not in chemsys: + raise ValueError( + f"The phase diagram chemical system must contain O and H! Your diagram chemical system is {chemsys}." + ) + + # ion_data = self.get_ion_reference_data_for_chemsys(chemsys) if not ion_ref_data else ion_ref_data + ion_data = ion_ref_data if ion_ref_data else self.get_ion_reference_data_for_chemsys(chemsys) + + # position the ion energies relative to most stable reference state + ion_entries = [] + for _, i_d in enumerate(ion_data): + ion = Ion.from_formula(i_d["formula"]) + refs = [e for e in pd.all_entries if e.composition.reduced_formula == i_d["data"]["RefSolid"]] + if not refs: + raise ValueError("Reference solid not contained in entry list") + stable_ref = sorted(refs, key=lambda x: x.energy_per_atom)[0] + rf = stable_ref.composition.get_reduced_composition_and_factor()[1] + + # TODO - need a more robust way to convert units + # use pint here? + if i_d["data"]["ΔGᶠRefSolid"]["unit"] == "kJ/mol": + # convert to eV/formula unit + ref_solid_energy = i_d["data"]["ΔGᶠRefSolid"]["value"] / 96.485 + elif i_d["data"]["ΔGᶠRefSolid"]["unit"] == "MJ/mol": + # convert to eV/formula unit + ref_solid_energy = i_d["data"]["ΔGᶠRefSolid"]["value"] / 96485 + else: + raise ValueError(f"Ion reference solid energy has incorrect unit {i_d['data']['ΔGᶠRefSolid']['unit']}") + solid_diff = pd.get_form_energy(stable_ref) - ref_solid_energy * rf + elt = i_d["data"]["MajElements"] + correction_factor = ion.composition[elt] / stable_ref.composition[elt] + # TODO - need a more robust way to convert units + # use pint here? + if i_d["data"]["ΔGᶠ"]["unit"] == "kJ/mol": + # convert to eV/formula unit + ion_free_energy = i_d["data"]["ΔGᶠ"]["value"] / 96.485 + elif i_d["data"]["ΔGᶠ"]["unit"] == "MJ/mol": + # convert to eV/formula unit + ion_free_energy = i_d["data"]["ΔGᶠ"]["value"] / 96485 + else: + raise ValueError(f"Ion free energy has incorrect unit {i_d['data']['ΔGᶠ']['unit']}") + energy = ion_free_energy + solid_diff * correction_factor + ion_entries.append(IonEntry(ion, energy)) + + return ion_entries + + # @classmethod + def get_pourbaix_entries( + self, + chemsys: str | list, + solid_compat="MaterialsProject2020Compatibility", + use_gibbs: Literal[300] | None = None, + ): + """A helper function to get all entries necessary to generate + a Pourbaix diagram from the rest interface. + + Args: + chemsys (str or [str]): Chemical system string comprising element + symbols separated by dashes, e.g., "Li-Fe-O" or List of element + symbols, e.g., ["Li", "Fe", "O"]. + solid_compat: Compatibility scheme used to pre-process solid DFT energies prior + to applying aqueous energy adjustments. May be passed as a class (e.g. + MaterialsProject2020Compatibility) or an instance + (e.g., MaterialsProject2020Compatibility()). If None, solid DFT energies + are used as-is. Default: MaterialsProject2020Compatibility + use_gibbs: Set to 300 (for 300 Kelvin) to use a machine learning model to + estimate solid free energy from DFT energy (see GibbsComputedStructureEntry). + This can slightly improve the accuracy of the Pourbaix diagram in some + cases. Default: None. Note that temperatures other than 300K are not + permitted here, because MaterialsProjectAqueousCompatibility corrections, + used in Pourbaix diagram construction, are calculated based on 300 K data. + """ + # imports are not top-level due to expense + from pymatgen.analysis.pourbaix_diagram import PourbaixEntry # noqa: PLC0415 + from pymatgen.entries.compatibility import ( # noqa: PLC0415 + Compatibility, + MaterialsProject2020Compatibility, + MaterialsProjectAqueousCompatibility, + MaterialsProjectCompatibility, + ) + from pymatgen.entries.computed_entries import ComputedEntry # noqa: PLC0415 + + if solid_compat == "MaterialsProjectCompatibility": + solid_compat = MaterialsProjectCompatibility() + elif solid_compat == "MaterialsProject2020Compatibility": + solid_compat = MaterialsProject2020Compatibility() + elif isinstance(solid_compat, Compatibility): + pass + else: + raise ValueError( + "Solid compatibility can only be 'MaterialsProjectCompatibility', " + "'MaterialsProject2020Compatibility', or an instance of a Compatibility class" + ) + + pbx_entries = [] + + if isinstance(chemsys, str): + chemsys = chemsys.split("-") + # capitalize and sort the elements + chemsys = sorted(e.capitalize() for e in chemsys) + + # Get ion entries first, because certain ions have reference + # solids that aren't necessarily in the chemsys (Na2SO4) + + # download the ion reference data from MPContribs + ion_data = self.get_ion_reference_data_for_chemsys(chemsys) + + # build the PhaseDiagram for get_ion_entries + ion_ref_comps = [Ion.from_formula(d["data"]["RefSolid"]).composition for d in ion_data] + ion_ref_elts = set(itertools.chain.from_iterable(i.elements for i in ion_ref_comps)) + # TODO - would be great if the commented line below would work + # However for some reason you cannot process GibbsComputedStructureEntry with + # MaterialsProjectAqueousCompatibility + + # if mpr is None: + # # raise ValueError("MPRester object is required") + # print("MPRester object is not provided, using default API key") + # api_key = os.getenv("MP_API_KEY", "12345678901234567890123456789012") + # mpr = MPRester(api_key=api_key) + + ion_ref_entries = self.mpr.get_entries_in_chemsys(list([str(e) for e in ion_ref_elts] + ["O", "H"])) + + # suppress the warning about supplying the required energies; they will be calculated from the + # entries we get from MPRester + with warnings.catch_warnings(): + warnings.filterwarnings( + "ignore", + message="You did not provide the required O2 and H2O energies.", + ) + compat = MaterialsProjectAqueousCompatibility(solid_compat=solid_compat) + # suppress the warning about missing oxidation states + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", message="Failed to guess oxidation states.*") + ion_ref_entries = compat.process_entries(ion_ref_entries) # type: ignore + # TODO - if the commented line above would work, this conditional block + # could be removed + if use_gibbs: + # replace the entries with GibbsComputedStructureEntry + from pymatgen.entries.computed_entries import GibbsComputedStructureEntry # noqa: PLC0415 + + ion_ref_entries = GibbsComputedStructureEntry.from_entries(ion_ref_entries, temp=use_gibbs) + ion_ref_pd = PhaseDiagram(ion_ref_entries) # type: ignore + + ion_entries = self.get_ion_entries(ion_ref_pd, ion_ref_data=ion_data) + pbx_entries = [PourbaixEntry(e, f"ion-{n}") for n, e in enumerate(ion_entries)] + + # Construct the solid pourbaix entries from filtered ion_ref entries + extra_elts = set(ion_ref_elts) - {Element(s) for s in chemsys} - {Element("H"), Element("O")} + for entry in ion_ref_entries: + entry_elts = set(entry.composition.elements) + # Ensure no OH chemsys or extraneous elements from ion references + if not (entry_elts <= {Element("H"), Element("O")} or extra_elts.intersection(entry_elts)): + # Create new computed entry + form_e = ion_ref_pd.get_form_energy(entry) # type: ignore + new_entry = ComputedEntry(entry.composition, form_e, entry_id=entry.entry_id) + pbx_entry = PourbaixEntry(new_entry) + pbx_entries.append(pbx_entry) + + return pbx_entries + + def generate_solution_objects(self): + """ + Args: + Parsing comp_dict to generate pyEQL solution objects + Returns: + List of pyEQL Solution components + """ + # TODO: Implement the Solution class here to process the comp_dict and do equilibrium calculations + ion_dict = self.comp_dict + default_units = "mol/L" + converted_ion_dict = { + standardize_formula(f"{ion}[{Element(ion).common_oxidation_states[0]:+d}]"): f"{val} {default_units}" + for ion, val in ion_dict.items() + } + + pH_values = [3, 7, 11] # pH sampling or do we need only one pH? + + speciated_ions = [] + for pH in pH_values: + sol = Solution(converted_ion_dict, pH=pH, balance_charge="auto") + sol.equilibrate() + ion_names = list(sol.components.keys()) + speciated_ions.append(ion_names) # get_amount that is higher than a threshold + + speciated_ions = list(set(itertools.chain.from_iterable(speciated_ions))) + + speciated_ions = [ + ion for ion in speciated_ions if ion not in ["H[+1]", "OH[-1]", "H2(aq)", "H2O(aq)", "O2(aq)"] + ] + + return speciated_ions # noqa: RET504 + + @staticmethod + def standardize_NBS_formula(x): + try: + return standardize_formula(x) + except Exception as e: + return f"ERROR: {e}" + + def NBS_table_ion_data(self): + """ + Docstring for NBS_table_data + Distinguish between a0 and ai! + + :param self: Description + """ + nbs_data = self.xlsx_path + df_dict = pd.read_excel(nbs_data, sheet_name="NBS Tables", skiprows=3) + df_dict = df_dict[df_dict.iloc[:, 4].isin(["ao", "ai"])] + df_dict.iloc[:, 0] = df_dict.iloc[:, 0].apply(self.standardize_NBS_formula) # Unnamed: 8 is delta G_f + + # Dictionary that maps the column names with formation energy + nbs_db = {} + for i in range(len(df_dict)): + identifier = df_dict.iloc[i, 0] + nbs_db[identifier] = { + "exp_form_E": {"value": df_dict.iloc[i, 8], "units": "kJ/mol"}, + "exp_entropy": {"value": df_dict.iloc[i, 9], "units": "J/(mol*K)"}, + } + + return nbs_db + + def modified_get_ion_reference_data_for_chemsys(self, chemsys: str | list, nbs_db: dict | None = None): + """ + Docstring for modified_get_ion_reference_data_for_chemsys + + :param self: Description + :param chemsys: Description + :type chemsys: str | list + """ + ion_data = loadfn(self.json_path) + ion_in_sol = self.generate_solution_objects() + + if nbs_db is None: + nbs_db = self.NBS_table_ion_data() + + if isinstance(chemsys, str): + chemsys = chemsys.split("-") + + # for [i, d] in enumerate(ion_data): + # if d['data']['MajElements'] == 'K': + # del ion_data[i] + + for entry in ion_in_sol: + identifier = entry + if identifier in ion_data: + continue + if identifier in nbs_db: + comp_name = identifier.split("[")[0].split("(")[0] + comp_name = Composition(comp_name) + maj_elements = [i.symbol for i in comp_name.elements if i.symbol not in ["H", "O"]] + + matched_ref_solids = [entry for entry in ion_data if entry["data"]["MajElements"] == maj_elements[0]] + + if matched_ref_solids: + ref_solid = matched_ref_solids[0]["data"]["RefSolid"] + ref_form_E_str = matched_ref_solids[0]["data"]["\u0394G\u1da0RefSolid"]["display"] + ref_form_E = float(matched_ref_solids[0]["data"]["\u0394G\u1da0RefSolid"]["value"]) + + if "[" in identifier and "]" in identifier: + charge_str = identifier[identifier.find("[") + 1 : identifier.find("]")] + charge = int(charge_str) + else: + charge_str = "0" + charge = 0 + + ion_record = { + "identifier": identifier, + "formula": identifier, + "data": { + "charge": {"display": charge_str, "value": charge, "unit": ""}, + "\u0394G\u1da0": { + "display": f"{nbs_db[identifier]['exp_form_E']['value']} {nbs_db[identifier]['exp_form_E']['units']}", + "value": float(nbs_db[identifier]["exp_form_E"]["value"]), + "unit": nbs_db[identifier]["exp_form_E"]["units"], + }, + "MajElements": maj_elements[0], + "RefSolid": ref_solid, + "\u0394G\u1da0RefSolid": {"display": ref_form_E_str, "value": ref_form_E, "unit": "kJ/mol"}, + "reference": "D. D. Wagman et al., Selected values of chemical thermodynamic properties, NBS Technical note 270, Washington; 1968-1971", + }, + } + ion_data.append(ion_record) + else: + print(f"Warning: {identifier} not found in NBS database.") + # pprint.pprint([d for d in ion_data if d["data"]["MajElements"] in chemsys]) + return [d for d in ion_data if d["data"]["MajElements"] in chemsys] diff --git a/src/pyEQL/pourbaix/pourbaix_diagram.py b/src/pyEQL/pourbaix/pourbaix_diagram.py new file mode 100644 index 00000000..c64ef871 --- /dev/null +++ b/src/pyEQL/pourbaix/pourbaix_diagram.py @@ -0,0 +1,1167 @@ +""" +This module is intended to be used to compute Pourbaix diagrams of arbitrary compositions +and formation energies. +""" + +from __future__ import annotations + +import itertools +import logging +import re +import warnings +from copy import deepcopy +from functools import cmp_to_key, partial +from multiprocessing import Pool +from typing import TYPE_CHECKING + +import numpy as np +from monty.json import MontyDecoder, MSONable +from pymatgen.analysis.phase_diagram import PDEntry, PhaseDiagram +from pymatgen.analysis.reaction_calculator import Reaction, ReactionError +from pymatgen.core import Composition, Element +from pymatgen.entries.computed_entries import ComputedEntry +from pymatgen.util.coord import Simplex +from pymatgen.util.due import Doi, due +from pymatgen.util.plotting import pretty_plot +from pymatgen.util.string import Stringify +from scipy.spatial import ConvexHull, HalfspaceIntersection +from scipy.special import comb + +from pyEQL.pourbaix.compatibility import MU_H2O +from pyEQL.pourbaix.ion import Ion + +if TYPE_CHECKING: + from collections.abc import Sequence + from typing import Any, ClassVar, Literal + + import matplotlib.pyplot as plt + from numpy.typing import NDArray + from pymatgen.core import DummySpecies, Species + from pymatgen.entries.computed_entries import ComputedStructureEntry + from typing_extensions import Self + +__author__ = "Sai Jayaraman" +__copyright__ = "Copyright 2012, The Materials Project" +__version__ = "0.4" +__maintainer__ = "Joseph Montoya" +__credits__ = "Arunima Singh, Joseph Montoya, Anjli Patel" +__email__ = "joseph.montoya@tri.global" +__status__ = "Production" +__date__ = "Nov 1, 2012" + +# If you use this module in your work, consider citing: +due.cite( + Doi("10.1103/PhysRevB.85.235438"), + description="Prediction of solid-aqueous equilibria: Scheme to combine first-principles " + "calculations of solids with experimental aqueous states", +) +due.cite( + Doi("10.1021/acs.chemmater.7b03980"), + description="Electrochemical Stability of Metastable Materials", +) +due.cite( + Doi("10.1021/acs.chemmater.7b03980"), + description="Fast computation of many-element Pourbaix diagrams", +) + +logger = logging.getLogger(__name__) + +PREFAC: float = 0.0591 # ln(10) * RT/nF in Nernst Equation + + +# TODO: Revise to more closely reflect PDEntry, invoke from energy/composition + +# TODO: PourbaixEntries depend implicitly on having entry energies be +# formation energies, should be a better way to get from raw energies + + +# TODO: uncorrected_energy is a bit of a misnomer, but not sure what to rename +class PourbaixEntry(MSONable, Stringify): + """ + An object encompassing all data relevant to a solid or ion + in a Pourbaix diagram. Each bulk solid/ion has an energy + g of the form: e = e0 + 0.0591 log10(conc) - nO mu_H2O + + (nH - 2nO) pH + phi (-nH + 2nO + q). + + Note that the energies corresponding to the input entries + should be formation energies with respect to hydrogen and + oxygen gas in order for the Pourbaix diagram formalism to + work. This may be changed to be more flexible in the future. + """ + + def __init__( + self, + entry: ComputedEntry | ComputedStructureEntry, + entry_id: str | None = None, + concentration: float = 1e-6, + ) -> None: + """ + Args: + entry (ComputedEntry | ComputedStructureEntry | PDEntry | IonEntry): An entry object + entry_id (str): A string id for the entry + concentration (float): Concentration of the entry in M. Defaults to 1e-6. + """ + self.entry = entry + self.correction = 0.0 + if isinstance(entry, IonEntry): + self.concentration = concentration + self.phase_type = "Ion" + self.charge = entry.ion.charge + else: + self.concentration = 1.0 + self.phase_type = "Solid" + self.charge = 0 + self.uncorrected_energy = entry.energy + if entry_id is not None: + self.entry_id: str | None = entry_id + elif getattr(entry, "entry_id", False): + self.entry_id = entry.entry_id + else: + self.entry_id = None + + def __repr__(self) -> str: + energy, npH, nPhi, nH2O, entry_id = ( + self.energy, + self.npH, + self.nPhi, + self.nH2O, + self.entry_id, + ) + return ( + f"{type(self).__name__}({self.entry.composition} with {energy=:.4f}, {npH=}, {nPhi=}, {nH2O=}, {entry_id=})" + ) + + @property + def npH(self) -> float: + """The number of H.""" + return self.entry.composition.get("H", 0) - 2 * self.entry.composition.get("O", 0) + + @property + def nH2O(self) -> float: + """The number of H2O.""" + return self.entry.composition.get("O", 0) + + @property + def nPhi(self) -> float: + """The number of electrons.""" + return self.npH - self.charge + + @npH.setter + def npH(self, value): + self._npH = value + + @property + def n_conc(self): + """The conc number used for 3D plots that vary concentration. 1 for ions, 0 for solids.""" + return int(isinstance(self.entry, IonEntry)) + + @property + def energy_without_conc_term(self): + """Total energy of the Pourbaix entry (at pH, V = 0 vs. SHE).""" + # Note: this implicitly depends on formation energies as input + return self.uncorrected_energy - (MU_H2O * self.nH2O) + + @property + def energy_without_phi_term(self) -> float: + """Total energy of the Pourbaix entry (at pH, V = 0 vs. SHE).""" + # Note: this implicitly depends on formation energies as input + return self.uncorrected_energy - (MU_H2O * self.nH2O) + (self.nPhi) * 0.0001 # voltage + + @property + def name(self) -> str: + """The entry's name.""" + if self.phase_type == "Solid": + return f"{self.entry.reduced_formula}(s)" + + return self.entry.name + + @property + def energy(self) -> float: + """Total energy of the Pourbaix entry (at pH, V = 0 vs. SHE).""" + # Note: this implicitly depends on formation energies as input + return self.uncorrected_energy + self.conc_term - (MU_H2O * self.nH2O) + + @property + def energy_per_atom(self) -> float: + """Energy per atom of the Pourbaix entry.""" + return self.energy / self.composition.num_atoms + + @property + def elements(self) -> list[Element | Species | DummySpecies]: + """Elements in the entry.""" + return self.entry.elements + + def energy_at_conditions(self, pH: float, V: float) -> float: + """Get free energy for a given pH and V. + + Args: + pH (float): pH at which to evaluate free energy + V (float): voltage at which to evaluate free energy + + Returns: + free energy at conditions + """ + return self.energy + self.npH * PREFAC * pH + self.nPhi * V + + def get_element_fraction(self, element: Element | str) -> float: + """Get the elemental fraction of a given non-OH element. + + Args: + element (Element or str): string or element corresponding + to element to get from composition + + Returns: + fraction of element / sum(all non-OH elements) + """ + return self.composition.get(element) * self.normalization_factor + + @property + def normalized_energy(self) -> float: + """Energy normalized by number of non H or O atoms, e.g. + for Zn2O6, energy / 2 or for AgTe3(OH)3, energy / 4. + """ + return self.energy * self.normalization_factor + + def normalized_energy_at_conditions(self, pH: float, V: float) -> float: + """Energy at an electrochemical condition, compatible with + numpy arrays for pH/V input. + + Args: + pH (float): pH at condition + V (float): applied potential at condition + + Returns: + energy normalized by number of non-O/H atoms at condition + """ + return self.energy_at_conditions(pH, V) * self.normalization_factor + + @property + def conc_term(self) -> float: + """The concentration contribution to the free energy. Should only be present + when there are ions in the entry. + """ + return PREFAC * np.log10(self.concentration) + + # TODO: not sure if these are strictly necessary with refactor + def as_dict(self): + """Get dict which contains Pourbaix Entry data. + Note that the pH, voltage, H2O factors are always calculated when + constructing a PourbaixEntry object. + """ + dct = { + "@module": type(self).__module__, + "@class": type(self).__name__, + "entry": self.entry.as_dict(), + "concentration": self.concentration, + "entry_id": self.entry_id, + } + if isinstance(self.entry, IonEntry): + dct["entry_type"] = "Ion" + else: + dct["entry_type"] = "Solid" + return dct + + @classmethod + def from_dict(cls, dct: dict) -> Self: + """Invokes a PourbaixEntry from a dictionary.""" + entry_type = dct["entry_type"] + entry = ( + IonEntry.from_dict(dct["entry"]) if entry_type == "Ion" else MontyDecoder().process_decoded(dct["entry"]) + ) + entry_id = dct["entry_id"] + concentration = dct["concentration"] + return cls(entry, entry_id, concentration) + + @property + def normalization_factor(self) -> float: + """Sum of number of atoms minus the number of H and O in composition.""" + return 1.0 / (self.num_atoms - self.composition.get("H", 0) - self.composition.get("O", 0)) + + @property + def composition(self) -> Composition: + """Composition.""" + return self.entry.composition + + @property + def num_atoms(self) -> float: + """Number of atoms in current formula. Useful for normalization.""" + return self.composition.num_atoms + + def to_pretty_string(self) -> str: + """A pretty string representation.""" + if self.phase_type == "Solid": + return f"{self.entry.reduced_formula}(s)" + + return self.entry.name + + +class MultiEntry(PourbaixEntry): + """PourbaixEntry-like object for constructing multi-elemental Pourbaix diagrams.""" + + def __init__(self, entry_list: Sequence[PourbaixEntry], weights: list[float] | None = None) -> None: + """Initialize a MultiEntry. + + Args: + entry_list (Sequence[PourbaixEntry]): Component PourbaixEntries. + weights (list[float]): Weights associated with each entry. Default is None + """ + self.weights = weights or [1.0] * len(entry_list) + self.entry_list = list(entry_list) + + def __getattr__(self, attr: str) -> Any: + """ + Because most of the attributes here are just weighted averages of the entry_list, + we save some space by having a set of conditionals to define the attributes. + """ + # Attributes that are weighted averages of entry attributes + if attr in { + "energy", + "npH", + "nH2O", + "nPhi", + "n_conc", + "conc_term", + "composition", + "uncorrected_energy", + "energy_without_conc_term", + "elements", + }: + # TODO: Composition could be changed for compat with sum + start = Composition() if attr == "composition" else 0 + weighted_values = ( + getattr(entry, attr) * weight for entry, weight in zip(self.entry_list, self.weights, strict=True) + ) + return sum(weighted_values, start) + + # Attributes that are just lists of entry attributes + if attr in {"entry_id", "phase_type"}: + return [getattr(entry, attr) for entry in self.entry_list] + + # normalization_factor, num_atoms should work from superclass + return self.__getattribute__(attr) + + def __repr__(self) -> str: + energy, npH, nPhi, nH2O, entry_id = ( + self.energy, + self.npH, + self.nPhi, + self.nH2O, + self.entry_id, + ) + cls_name, species = type(self).__name__, self.name + return f"Pourbaix{cls_name}({energy=:.4f}, {npH=}, {nPhi=}, {nH2O=}, {entry_id=}, {species=})" + + @property + def name(self) -> str: + """MultiEntry name, i.e. the name of each entry joined by ' + '.""" + return " + ".join(entry.name for entry in self.entry_list) + + def as_dict(self) -> dict[str, Any]: + """Get MSONable dict.""" + return { + "@module": type(self).__module__, + "@class": type(self).__name__, + "entry_list": [entry.as_dict() for entry in self.entry_list], + "weights": self.weights, + } + + @classmethod + def from_dict(cls, dct: dict) -> Self: + """ + Args: + dct (dict): Dict representation. + + Returns: + MultiEntry + """ + entry_list = [PourbaixEntry.from_dict(entry) for entry in dct.get("entry_list", ())] + return cls(entry_list, dct.get("weights")) + + +# TODO: this class isn't particularly useful in its current form, could be +# refactored to include information about the reference solid +class IonEntry(PDEntry): + """ + Object similar to PDEntry, but contains an Ion object instead of a + Composition object. + + Attributes: + name (str): A name for the entry. This is the string shown in the phase diagrams. + By default, this is the reduced formula for the composition, but can be + set to some other string for display purposes. + """ + + def __init__(self, ion: Ion, energy: float, name: str | None = None, attribute=None): + """ + Args: + ion: Ion object + energy: Energy for composition. + name: Optional parameter to name the entry. Defaults to the + chemical formula. + attribute: Optional attribute of the entry, e.g. band gap. + """ + self.ion = ion + # Auto-assign name + name = name or self.ion.reduced_formula + super().__init__(composition=ion.composition, energy=energy, name=name, attribute=attribute) + + def __repr__(self) -> str: + return f"IonEntry : {self.composition} with energy = {self.energy:.4f}" + + @classmethod + def from_dict(cls, dct: dict) -> Self: + """Get an IonEntry object from a dict.""" + return cls( + Ion.from_dict(dct["ion"]), + dct["energy"], + dct.get("name"), + dct.get("attribute"), + ) + + def as_dict(self) -> dict[Literal["ion", "energy", "name"], Any]: + """Create a dict of composition, energy, and ion name.""" + return {"ion": self.ion.as_dict(), "energy": self.energy, "name": self.name} + + +def ion_or_solid_comp_object(formula: str) -> Composition | Ion: + """Get an Ion or Composition from a formula. + + Args: + formula (str): Formula. E.g. of ion: NaOH(aq), Na[+], Na+; + E.g. of solid: Fe2O3(s), Fe(s), Na2O. + + Returns: + Composition/Ion object. + """ + # Formula for ion + if formula.endswith("(aq)") or re.search(r"\[.*\]$", formula) or "-" in formula or "+" in formula: + return Ion.from_formula(formula) + + # Formula for solid + if formula.endswith("(s)"): + return Composition(formula[:-3]) + + return Composition(formula) + + +# TODO: the solids filter breaks some of the functionality of the +# heatmap plotter, because the reference states for decomposition +# don't include oxygen/hydrogen in the OER/HER regions + + +# TODO: create a from_phase_diagram class method for non-formation energy invocation +# TODO: invocation from a MultiEntry entry list could be a bit more robust +# TODO: serialization is still a bit rough around the edges +class PourbaixDiagram(MSONable): + """Create a Pourbaix diagram from entries.""" + + elements_ho: ClassVar[set[Element]] = {Element("H"), Element("O")} + + def __init__( + self, + entries: list[PourbaixEntry] | list[MultiEntry], + comp_dict: list[dict[str, float]] | dict[str, float] | None = None, + conc_dict: dict[str, float] | None = None, + filter_solids: bool = True, + nproc: int | None = None, + ) -> None: + """ + Args: + entries ([PourbaixEntry] or [MultiEntry]): Entries list + containing Solids and Ions or a list of MultiEntries + comp_dict (dict[str, float]): Dictionary of compositions, + defaults to equal parts of each elements + conc_dict (dict[str, float]): Dictionary of ion concentrations, + defaults to 1e-6 for each element + filter_solids (bool): applying this filter to a Pourbaix + diagram ensures all included solid phases are filtered by + stability on the compositional phase diagram. Defaults to True. + The practical consequence of this is that highly oxidized or reduced + phases that might show up in experiments due to kinetic limitations + on oxygen/hydrogen evolution won't appear in the diagram, but they are + not actually "stable" (and are frequently overstabilized from DFT errors). + Hence, including only the stable solid phases generally leads to the + most accurate Pourbaix diagrams. + nproc (int): number of processes to generate multi-entries with + in parallel. Defaults to None (serial processing). + """ + entries = deepcopy(entries) + self.filter_solids = filter_solids + + # Get non-OH elements + self.pbx_elts = list( + set(itertools.chain.from_iterable([entry.composition.elements for entry in entries])) - self.elements_ho + ) + self.dim = len(self.pbx_elts) - 1 + + # Process multi-entry inputs + if isinstance(entries[0], MultiEntry): + self._processed_entries = entries + # Extract individual entries + single_entries = list(set(itertools.chain.from_iterable([entry.entry_list for entry in entries]))) + self._unprocessed_entries = single_entries + self._filtered_entries = single_entries + self._conc_dict = None + self._elt_comp = {k: v for k, v in entries[0].composition.items() if k not in self.elements_ho} + self._multi_element = True + + # Process single entry inputs + else: + # Set default conc/comp dicts + if not comp_dict: + comp_dict = {elt.symbol: 1 / len(self.pbx_elts) for elt in self.pbx_elts} + if not conc_dict: + conc_dict = {elt.symbol: 1e-6 for elt in self.pbx_elts} + self._conc_dict = conc_dict + + self._elt_comp = comp_dict + self.pourbaix_elements = self.pbx_elts + + solid_entries = [entry for entry in entries if entry.phase_type == "Solid"] + ion_entries = [entry for entry in entries if entry.phase_type == "Ion"] + + # If a conc_dict is specified, override individual entry concentrations + for entry in ion_entries: + ion_elts = list(set(entry.elements) - self.elements_ho) + # TODO: the logic here for ion concentration setting is in two + # places, in PourbaixEntry and here, should be consolidated + if len(ion_elts) == 1: + entry.concentration = conc_dict[ion_elts[0].symbol] * entry.normalization_factor + elif len(ion_elts) > 1 and not entry.concentration: + raise ValueError("Elemental concentration not compatible with multi-element ions") + + self._unprocessed_entries = solid_entries + ion_entries + + if len(solid_entries + ion_entries) != len(entries): + raise ValueError('All supplied entries must have a phase type of either "Solid" or "Ion"') + + if self.filter_solids: + # O is 2.46 b/c pbx entry finds energies referenced to H2O + entries_HO = [ComputedEntry("H", 0), ComputedEntry("O", 2.46)] + solid_pd = PhaseDiagram(solid_entries + entries_HO) + solid_entries = list(set(solid_pd.stable_entries) - set(entries_HO)) + + self._filtered_entries = solid_entries + ion_entries + if not isinstance(comp_dict, list): + comp_dict = [comp_dict] + + self._processed_entries = [] + for sub_comp_dict in comp_dict: + if len(sub_comp_dict) > 1: + self._multi_element = True + entries = self._preprocess_pourbaix_entries(self._filtered_entries, nproc=nproc) + else: + self._multi_element = False + entries = self._filtered_entries + self._processed_entries.extend(entries) + + self._stable_domains, self._stable_domain_vertices = self.get_pourbaix_domains(self._processed_entries) + + def _convert_entries_to_points(self, pourbaix_entries: list[PourbaixEntry]) -> NDArray: + """ + Args: + pourbaix_entries (list[PourbaixEntry]): Pourbaix entries + to process into vectors in nph-nphi-composition space. + + Returns: + NDAarray: vectors as [[nph, nphi, e0, x1, x2, ..., xn-1]] + corresponding to each entry in nph-nphi-composition space + """ + vecs = [ + [entry.npH, entry.nPhi, entry.energy] + [entry.composition.get(elt) for elt in self.pbx_elts[:-1]] + for entry in pourbaix_entries + ] + vecs = np.array(vecs) + norms = np.transpose([[entry.normalization_factor for entry in pourbaix_entries]]) + vecs *= norms + return vecs + + def _get_hull_in_nph_nphi_space( + self, + entries: list[PourbaixEntry], + ) -> tuple[list[PourbaixEntry], list[Simplex]]: + """Generate convex hull of Pourbaix diagram entries in composition, + npH, and nphi space. This enables filtering of multi-entries + such that only compositionally stable combinations of entries + are included. + + Args: + entries (list[PourbaixEntry]): PourbaixEntries to construct + the convex hull. + + Returns: + tuple[list[PourbaixEntry], list[Simplex]]: PourbaixEntry list and stable + facets corresponding to that list + """ + ion_entries = [entry for entry in entries if entry.phase_type == "Ion"] + solid_entries = [entry for entry in entries if entry.phase_type == "Solid"] + + # Pre-filter solids based on min at each composition + logger.debug("Pre-filtering solids by min energy at each composition") + sorted_entries = sorted( + solid_entries, + key=lambda x: (x.composition.reduced_composition, x.entry.energy_per_atom), + ) + grouped_by_composition = itertools.groupby(sorted_entries, key=lambda x: x.composition.reduced_composition) + min_entries = [next(iter(grouped_entries)) for comp, grouped_entries in grouped_by_composition] + min_entries += ion_entries + + logger.debug("Constructing nph-nphi-composition points for qhull") + + vecs = self._convert_entries_to_points(min_entries) + maxes = np.max(vecs[:, :3], axis=0) + extra_point = np.concatenate([maxes, np.ones(self.dim) / self.dim], axis=0) + + # Add padding for extra point + pad = 1000 + extra_point[2] += pad + points = np.concatenate([vecs, np.array([extra_point])], axis=0) + logger.debug("Constructing convex hull in nph-nphi-composition space") + hull = ConvexHull(points, qhull_options="QJ i") + + # Create facets and remove top + facets = [facet for facet in hull.simplices if len(points) - 1 not in facet] + + if self.dim > 1: + logger.debug("Filtering facets by Pourbaix composition") + valid_facets = [] + for facet in facets: + comps = vecs[facet][:, 3:] + full_comps = np.concatenate([comps, 1 - np.sum(comps, axis=1).reshape(len(comps), 1)], axis=1) + # Ensure an compositional interior point exists in the simplex + if np.linalg.matrix_rank(full_comps) > self.dim: + valid_facets.append(facet) + else: + valid_facets = facets + + return min_entries, valid_facets + + def _preprocess_pourbaix_entries( + self, + entries: list[PourbaixEntry], + nproc: int | None = None, + ) -> list[MultiEntry]: + """Generate multi-entries for Pourbaix diagram. + + Args: + entries (list[PourbaixEntry]): PourbaixEntries to preprocess + into MultiEntries + nproc (int): number of processes to be used in parallel + treatment of entry combos + + Returns: + list[MultiEntry]: stable MultiEntry candidates + """ + + min_entries, valid_facets = self._get_hull_in_nph_nphi_space(entries) + + combos: list[list[frozenset]] = [] + for facet in valid_facets: + for idx in range(1, self.dim + 2): + these_combos: list[frozenset] = [] + for combo in itertools.combinations(facet, idx): + these_entries = [min_entries[i] for i in combo] + these_combos.append(frozenset(these_entries)) + combos.append(these_combos) + + all_combos: set | list = set(itertools.chain.from_iterable(combos)) + + # Use list comprehension for all_combos + all_combos = [list(combo) for combo in all_combos] + + elt_comps = self._elt_comp if isinstance(self._elt_comp, list) else [self._elt_comp] + + multi_entries: list = [] + + # Parallel processing of multi-entry generation + for sub_comp in elt_comps: + tot_comp = Composition(sub_comp) + if nproc is not None: + func = partial(self.process_multientry, prod_comp=tot_comp) + with Pool(nproc) as proc_pool: + multi_entries = list(proc_pool.imap(func, all_combos)) + multi_entries = list(filter(bool, multi_entries)) + else: + # Serial processing of multi-entry generation + for combo in all_combos: + if multi_entry := self.process_multientry(combo, prod_comp=tot_comp): + multi_entries.append(multi_entry) + + return multi_entries + + def _generate_multielement_entries( + self, + entries: list[PourbaixEntry], + nproc: int | None = None, + ) -> list[MultiEntry]: + """ + Create entries for multi-element Pourbaix construction. + + This works by finding all possible linear combinations + of entries that can result in the specified composition + from the initialized comp_dict. + + Args: + entries ([PourbaixEntries]): list of Pourbaix entries + to process into MultiEntries + nproc (int): number of processes to be used in parallel + treatment of entry combos + """ + n_elems = len(self._elt_comp) # No. of elements + total_comp = Composition(self._elt_comp) + + # generate all combinations of compounds that have all elements + entry_combos: list = [itertools.combinations(entries, idx + 1) for idx in range(n_elems)] + entry_combos = list(itertools.chain.from_iterable(entry_combos)) + + entry_combos = list(filter(lambda x: total_comp < MultiEntry(x).composition, entry_combos)) + + # Generate and filter entries + processed_entries: list = [] + total = sum(comb(len(entries), idx + 1) for idx in range(n_elems)) + if total > 1e6: + warnings.warn( + f"Your Pourbaix diagram includes {total} entries and may take a long time to generate.", stacklevel=2 + ) + + # Parallel processing of multi-entry generation + if nproc is not None: + func = partial(self.process_multientry, prod_comp=total_comp) + with Pool(nproc) as proc_pool: + processed_entries = list(proc_pool.imap(func, entry_combos)) + processed_entries = list(filter(bool, processed_entries)) + # Serial processing of multi-entry generation + else: + for combo in entry_combos: + processed_entry = self.process_multientry(combo, total_comp) + if processed_entry is not None: + processed_entries.append(processed_entry) + + return processed_entries + + @staticmethod + def process_multientry( + entry_list: Sequence, prod_comp: Composition, coeff_threshold: float = 1e-4 + ) -> MultiEntry | None: + """Static method for finding a multientry based on + a list of entries and a product composition. + Essentially checks to see if a valid aqueous + reaction exists between the entries and the + product composition and returns a MultiEntry + with weights according to the coefficients if so. + + Args: + entry_list (Sequence[Entry]): Entries from which to + create a MultiEntry + prod_comp (Composition): composition constraint for setting + weights of MultiEntry + coeff_threshold (float): threshold of stoichiometric + coefficients to filter, if weights are lower than + this value, the entry is not returned + """ + dummy_oh = [Composition("H"), Composition("O")] + try: + # Get balanced reaction coeffs, ensuring all < 0 or conc thresh + # Note that we get reduced compositions for solids and non-reduced + # compositions for ions because ions aren't normalized due to + # their charge state. + entry_comps = [entry.composition for entry in entry_list] + rxn = Reaction(entry_comps + dummy_oh, [prod_comp]) + react_coeffs = [-coeff for coeff in rxn.coeffs[: len(entry_list)]] + all_coeffs = [*react_coeffs, rxn.get_coeff(prod_comp)] + + # Check if reaction coeff threshold met for Pourbaix compounds + # All reactant/product coefficients must be positive nonzero + if all(coeff > coeff_threshold for coeff in all_coeffs): + return MultiEntry(entry_list, weights=react_coeffs) + + return None + except ReactionError: + return None + + @staticmethod + def get_pourbaix_domains( + pourbaix_entries: list[PourbaixEntry], + limits: list[list[float]] | None = None, + ) -> tuple[dict, dict]: + """Get a set of Pourbaix stable domains (i.e. polygons) in + pH-V space from a list of pourbaix_entries. + + This function works by using scipy's HalfspaceIntersection + function to construct all of the 2-D polygons that form the + boundaries of the planes corresponding to individual entry + gibbs free energies as a function of pH and V. Hyperplanes + of the form a*pH + b*V + 1 - g(0, 0) are constructed and + supplied to HalfspaceIntersection, which then finds the + boundaries of each Pourbaix region using the intersection + points. + + Args: + pourbaix_entries (list[PourbaixEntry]): Pourbaix entries + with which to construct stable Pourbaix domains + limits (list[list[float]]): limits in which to do the pourbaix + analysis + + Returns: + tuple[dict[PourbaixEntry, list], dict[PourbaixEntry, NDArray]: + The first dict is of form: {entry: [boundary_points]}. + The list of boundary points are the sides of the N-1 + dim polytope bounding the allowable ph-V range of each entry. + """ + if limits is None: + limits = [[-2, 16], [-4, 4]] + + # Get hyperplanes + hyperplanes = np.array( + [ + np.array([-PREFAC * entry.npH, -entry.nPhi, 0, -entry.energy]) * entry.normalization_factor + for entry in pourbaix_entries + ] + ) + hyperplanes[:, 2] = 1 + + max_contribs = np.max(np.abs(hyperplanes), axis=0) + g_max = np.dot(-max_contribs, [limits[0][1], limits[1][1], 0, 1]) + + # Add border hyperplanes and generate HalfspaceIntersection + border_hyperplanes = [ + [-1, 0, 0, limits[0][0]], + [1, 0, 0, -limits[0][1]], + [0, -1, 0, limits[1][0]], + [0, 1, 0, -limits[1][1]], + [0, 0, -1, 2 * g_max], + ] + hs_hyperplanes = np.vstack([hyperplanes, border_hyperplanes]) + interior_point = [*np.mean(limits, axis=1).tolist(), g_max] + hs_int = HalfspaceIntersection(hs_hyperplanes, np.array(interior_point)) + + # organize the boundary points by entry + pourbaix_domains: dict[PourbaixEntry, list] = {entry: [] for entry in pourbaix_entries} + for intersection, facet in zip(hs_int.intersections, hs_int.dual_facets, strict=True): + for v in facet: + if v < len(pourbaix_entries): + this_entry = pourbaix_entries[v] + pourbaix_domains[this_entry].append(intersection) + + # Remove entries with no Pourbaix region + pourbaix_domains = {k: v for k, v in pourbaix_domains.items() if v} + pourbaix_domain_vertices: dict[PourbaixEntry, NDArray[float]] = {} + + for entry, points in pourbaix_domains.items(): + points = np.array(points)[:, :2] + # Initial sort to ensure consistency + points = points[np.lexsort(np.transpose(points))] + center: NDArray[float] = np.mean(points, axis=0) + points_centered: NDArray[float] = points - center + + # Sort points by cross product of centered points, + # isn't strictly necessary but useful for plotting tools + points_centered = sorted( + points_centered, + key=cmp_to_key(lambda x, y: x[0] * y[1] - x[1] * y[0]), # type: ignore[index] + ) + points = points_centered + center + + # Create simplices corresponding to Pourbaix boundary + simplices = [Simplex(points[indices]) for indices in ConvexHull(points).simplices] + pourbaix_domains[entry] = simplices + pourbaix_domain_vertices[entry] = points + + return pourbaix_domains, pourbaix_domain_vertices + + def find_stable_entry(self, pH: float, V: float) -> PourbaixEntry: + """Find stable entry at a pH,V condition. + + Args: + pH (float): pH to find stable entry + V (float): V to find stable entry. + + Returns: + PourbaixEntry: stable entry at pH, V + """ + energies_at_conditions = [entry.normalized_energy_at_conditions(pH, V) for entry in self.stable_entries] + return self.stable_entries[np.argmin(energies_at_conditions)] + + def get_decomposition_energy( + self, + entry: PourbaixEntry, + pH: float, + V: float, + ) -> NDArray: + """Find decomposition to most stable entries in eV/atom, + supports vectorized inputs for pH and V. + + Args: + entry (PourbaixEntry): PourbaixEntry corresponding to + compound to find the decomposition for + pH (float): pH at which to find the decomposition + V (float): voltage at which to find the decomposition + + Returns: + Decomposition energy for the entry, i.e. the energy above + the "Pourbaix hull" in eV/atom at the given conditions + """ + # Check composition consistency between entry and Pourbaix diagram: + pbx_comp = Composition(self._elt_comp).fractional_composition + entry_pbx_comp = Composition( + {elt: coeff for elt, coeff in entry.composition.items() if elt not in self.elements_ho} + ).fractional_composition + if entry_pbx_comp != pbx_comp: + raise ValueError("Composition of stability entry does not match Pourbaix Diagram") + entry_normalized_energy = entry.normalized_energy_at_conditions(pH, V) + hull_energy = self.get_hull_energy(pH, V) + decomposition_energy = entry_normalized_energy - hull_energy + + # Convert to eV/atom instead of eV/normalized formula unit + decomposition_energy /= entry.normalization_factor + decomposition_energy /= entry.composition.num_atoms + return decomposition_energy + + def get_hull_energy(self, pH: float | list[float], V: float | list[float]) -> NDArray: + """Get the minimum energy of the Pourbaix "basin" that is formed + from the stable Pourbaix planes. Vectorized. + + Args: + pH (float | list[float]): pH at which to find the hull energy + V (float | list[float]): V at which to find the hull energy + + Returns: + np.array: minimum Pourbaix energy at conditions + """ + all_gs = np.array([entry.normalized_energy_at_conditions(pH, V) for entry in self.stable_entries]) + return np.min(all_gs, axis=0) + + def get_stable_entry(self, pH: float, V: float) -> PourbaixEntry | MultiEntry: + """Get the stable entry at a given pH, V condition. + + Args: + pH (float): pH at a given condition + V (float): V at a given condition + + Returns: + PourbaixEntry | MultiEntry: Pourbaix or multi-entry + corresponding to the minimum energy entry at a given pH, V condition + """ + all_gs = np.array([entry.normalized_energy_at_conditions(pH, V) for entry in self.stable_entries]) + return self.stable_entries[np.argmin(all_gs)] + + @property + def stable_entries(self) -> list: + """The stable entries in the Pourbaix diagram.""" + return list(self._stable_domains) + + @property + def unstable_entries(self) -> list: + """All unstable entries in the Pourbaix diagram.""" + return [entry for entry in self.all_entries if entry not in self.stable_entries] + + @property + def all_entries(self) -> list: + """All entries used to generate the Pourbaix diagram.""" + return self._processed_entries + + @property + def unprocessed_entries(self) -> list: + """Unprocessed entries.""" + return self._unprocessed_entries + + def as_dict(self) -> dict[str, Any]: + """Get MSONable dict.""" + return { + "@module": type(self).__module__, + "@class": type(self).__name__, + "entries": [entry.as_dict() for entry in self._unprocessed_entries], + "comp_dict": self._elt_comp, + "conc_dict": self._conc_dict, + "filter_solids": self.filter_solids, + } + + @classmethod + def from_dict(cls, dct: dict) -> Self: + """ + Args: + dct (dict): Dict representation. + + Returns: + PourbaixDiagram + """ + decoded_entries = MontyDecoder().process_decoded(dct["entries"]) + return cls( + decoded_entries, + comp_dict=dct.get("comp_dict"), + conc_dict=dct.get("conc_dict"), + filter_solids=bool(dct.get("filter_solids")), + ) + + +class PourbaixPlotter: + """A plotter class for phase diagrams.""" + + def __init__(self, pourbaix_diagram: PourbaixDiagram) -> None: + """ + Args: + pourbaix_diagram (PourbaixDiagram): A PourbaixDiagram object. + """ + self._pbx = pourbaix_diagram + + @staticmethod + def _generate_entry_label(entry: PourbaixEntry | MultiEntry) -> str: + """ + Generates a label for the Pourbaix plotter. + + Args: + entry (PourbaixEntry or MultiEntry): entry to get a label for + """ + if isinstance(entry, MultiEntry): + return " + ".join(entry.name for entry in entry.entry_list) + + # TODO - a more elegant solution could be added later to Stringify + # for example, the pattern re.sub(r"([-+][\d\.]*)", r"$^{\1}$", ) + # will convert B(OH)4- to B(OH)$_4^-$. + # for this to work, the ion's charge always must be written AFTER + # the sign (e.g., Fe+2 not Fe2+) + string = entry.to_latex_string() + return re.sub(r"()\[([^)]*)\]", r"\1$^{\2}$", string) + + def show(self, *args, **kwargs) -> None: + """Show the Pourbaix plot. + + Args: + *args: args to get_pourbaix_plot + **kwargs: kwargs to get_pourbaix_plot + """ + plt = self.get_pourbaix_plot(*args, **kwargs) + plt.show() + + def get_pourbaix_plot( + self, + limits: tuple[tuple[float, float], tuple[float, float]] | None = None, + title: str = "", + label_domains: bool = True, + label_fontsize: int = 20, + show_water_lines: bool = True, + show_neutral_axes: bool = True, + ax: plt.Axes = None, + ) -> plt.Axes: + """ + Plot Pourbaix diagram. + + Args: + limits: tuple containing limits of the Pourbaix diagram + of the form ((xlo, xhi), (ylo, yhi)). + title (str): Title to display on plot + label_domains (bool): whether to label Pourbaix domains + label_fontsize: font size for domain labels + show_water_lines: whether to show dashed lines indicating the region + of water stability. + show_neutral_axes; whether to show dashed horizontal and vertical lines + at 0 V and pH 7, respectively. + ax (Axes): Matplotlib Axes instance for plotting + + Returns: + Axes: matplotlib Axes object with Pourbaix diagram + """ + if limits is None: + limits = ((-2, 16), (-3, 3)) + + ax = ax or pretty_plot(16) + + xlim, ylim = limits + lw = 3 + + if show_water_lines: + h_line = np.transpose([[xlim[0], -xlim[0] * PREFAC], [xlim[1], -xlim[1] * PREFAC]]) + o_line = np.transpose( + [ + [xlim[0], -xlim[0] * PREFAC + 1.23], + [xlim[1], -xlim[1] * PREFAC + 1.23], + ] + ) + ax.plot(h_line[0], h_line[1], "r--", linewidth=lw) + ax.plot(o_line[0], o_line[1], "r--", linewidth=lw) + + if show_neutral_axes: + neutral_line = np.transpose([[7, ylim[0]], [7, ylim[1]]]) + V0_line = np.transpose([[xlim[0], 0], [xlim[1], 0]]) + ax.plot(neutral_line[0], neutral_line[1], "k-.", linewidth=lw) + ax.plot(V0_line[0], V0_line[1], "k-.", linewidth=lw) + + for entry, vertices in self._pbx._stable_domain_vertices.items(): + center = np.mean(vertices, axis=0) + x, y = np.transpose(np.vstack([vertices, vertices[0]])) + ax.plot(x, y, "k-", linewidth=lw) + + if label_domains: + ax.annotate( + self._generate_entry_label(entry), + center, + ha="center", + va="center", + fontsize=label_fontsize, + color="b", + ).draggable() + + ax.set_title(title, fontsize=20, fontweight="bold") + ax.set(xlabel="pH", ylabel="E (V)", xlim=xlim, ylim=ylim) + return ax + + def plot_entry_stability( + self, + entry: Any, + pH_range: tuple[float, float] = (-2, 16), + pH_resolution: int = 100, + V_range: tuple[float, float] = (-3, 3), + V_resolution: int = 100, + e_hull_max: float = 1, + cmap: str = "RdYlBu_r", + ax: plt.Axes | None = None, + **kwargs: Any, + ) -> plt.Axes: + """ + Plots the stability of an entry in the Pourbaix diagram. + + Args: + entry (Any): The entry to plot stability for. + pH_range (tuple[float, float]): pH range for the plot. Defaults to (-2, 16). + pH_resolution (int): pH resolution. Defaults to 100. + V_range (tuple[float, float]): Voltage range for the plot. Defaults to (-3, 3). + V_resolution (int): Voltage resolution. Defaults to 100. + e_hull_max (float): Maximum energy above the hull. Defaults to 1. + cmap (str): Colormap for the plot. Defaults to "RdYlBu_r". + ax (Axes, optional): Existing matplotlib Axes object for plotting. Defaults to None. + **kwargs (Any): Additional keyword arguments passed to `get_pourbaix_plot`. + + Returns: + plt.Axes: Matplotlib Axes object with the plotted stability. + """ + # Plot the Pourbaix diagram + ax = self.get_pourbaix_plot(ax=ax, **kwargs) + pH, V = np.mgrid[ + pH_range[0] : pH_range[1] : pH_resolution * 1j, # type: ignore[misc] + V_range[0] : V_range[1] : V_resolution * 1j, # type: ignore[misc] + ] + + stability = self._pbx.get_decomposition_energy(entry, pH, V) + + # Plot stability map + cax = ax.pcolor(pH, V, stability, cmap=cmap, vmin=0, vmax=e_hull_max) + cbar = ax.figure.colorbar(cax) + cbar.set_label(f"Stability of {self._generate_entry_label(entry)} (eV/atom)") + + # Set ticklabels + # ticklabels = [t.get_text() for t in cbar.ax.get_yticklabels()] + # ticklabels[-1] = f">={ticklabels[-1]}" + # cbar.ax.set_yticklabels(ticklabels) + + return ax + + def domain_vertices(self, entry) -> list: + """Get the vertices of the Pourbaix domain. + + Args: + entry: Entry for which domain vertices are desired + + Returns: + list of vertices + """ + return self._pbx._stable_domain_vertices[entry] diff --git a/src/pyEQL/pourbaix/pourbaix_test_data.json b/src/pyEQL/pourbaix/pourbaix_test_data.json new file mode 100644 index 00000000..48cbc667 --- /dev/null +++ b/src/pyEQL/pourbaix/pourbaix_test_data.json @@ -0,0 +1,13900 @@ +{ + "Ag-Te": [ + { + "@class": "PourbaixEntry", + "@module": "pymatgen.analysis.pourbaix_diagram", + "@version": null, + "concentration": 1e-06, + "entry": { + "energy": 0.5200090748504765, + "ion": { + "Ag": 1.0, + "charge": 1.0 + }, + "name": "Ag[+]" + }, + "entry_id": "ion-0", + "entry_type": "Ion" + }, + { + "@class": "PourbaixEntry", + "@module": "pymatgen.analysis.pourbaix_diagram", + "@version": null, + "concentration": 1e-06, + "entry": { + "energy": -1.2314489251495235, + "ion": { + "Ag": 1.0, + "H": 1.0, + "O": 1.0, + "charge": 0.0 + }, + "name": "AgHO(aq)" + }, + "entry_id": "ion-1", + "entry_type": "Ion" + }, + { + "@class": "PourbaixEntry", + "@module": "pymatgen.analysis.pourbaix_diagram", + "@version": null, + "concentration": 1e-06, + "entry": { + "energy": -2.9751859251495234, + "ion": { + "Ag": 1.0, + "H": 2.0, + "O": 2.0, + "charge": -1.0 + }, + "name": "Ag(HO)2[-]" + }, + "entry_id": "ion-2", + "entry_type": "Ion" + }, + { + "@class": "PourbaixEntry", + "@module": "pymatgen.analysis.pourbaix_diagram", + "@version": null, + "concentration": 1e-06, + "entry": { + "energy": 2.4781402888504767, + "ion": { + "Ag": 1.0, + "charge": 2.0 + }, + "name": "Ag[2+]" + }, + "entry_id": "ion-3", + "entry_type": "Ion" + }, + { + "@class": "PourbaixEntry", + "@module": "pymatgen.analysis.pourbaix_diagram", + "@version": null, + "concentration": 1e-06, + "entry": { + "energy": 0.38545476825000025, + "ion": { + "H": 2.0, + "Te": 1.0, + "charge": 0.0 + }, + "name": "TeH2(aq)" + }, + "entry_id": "ion-4", + "entry_type": "Ion" + }, + { + "@class": "PourbaixEntry", + "@module": "pymatgen.analysis.pourbaix_diagram", + "@version": null, + "concentration": 1e-06, + "entry": { + "energy": 0.5415631592500003, + "ion": { + "H": 1.0, + "Te": 1.0, + "charge": -1.0 + }, + "name": "TeH[-]" + }, + "entry_id": "ion-5", + "entry_type": "Ion" + }, + { + "@class": "PourbaixEntry", + "@module": "pymatgen.analysis.pourbaix_diagram", + "@version": null, + "concentration": 1e-06, + "entry": { + "energy": -6.02973284375, + "ion": { + "H": 2.0, + "O": 3.0, + "Te": 1.0, + "charge": 0.0 + }, + "name": "TeH2O3(aq)" + }, + "entry_id": "ion-6", + "entry_type": "Ion" + }, + { + "@class": "PourbaixEntry", + "@module": "pymatgen.analysis.pourbaix_diagram", + "@version": null, + "concentration": 1e-06, + "entry": { + "energy": -5.61778014475, + "ion": { + "H": 1.0, + "O": 3.0, + "Te": 1.0, + "charge": -1.0 + }, + "name": "TeHO3[-]" + }, + "entry_id": "ion-7", + "entry_type": "Ion" + }, + { + "@class": "PourbaixEntry", + "@module": "pymatgen.analysis.pourbaix_diagram", + "@version": null, + "concentration": 1e-06, + "entry": { + "energy": -5.16029583175, + "ion": { + "O": 3.0, + "Te": 1.0, + "charge": -2.0 + }, + "name": "TeO3[2-]" + }, + "entry_id": "ion-8", + "entry_type": "Ion" + }, + { + "@class": "PourbaixEntry", + "@module": "pymatgen.analysis.pourbaix_diagram", + "@version": null, + "concentration": 1e-06, + "entry": { + "energy": -6.80246938075, + "ion": { + "H": 2.0, + "O": 4.0, + "Te": 1.0, + "charge": 0.0 + }, + "name": "Te(HO2)2(aq)" + }, + "entry_id": "ion-9", + "entry_type": "Ion" + }, + { + "@class": "PourbaixEntry", + "@module": "pymatgen.analysis.pourbaix_diagram", + "@version": null, + "concentration": 1e-06, + "entry": { + "energy": -6.4386501017499995, + "ion": { + "H": 1.0, + "O": 4.0, + "Te": 1.0, + "charge": -1.0 + }, + "name": "TeHO4[-]" + }, + "entry_id": "ion-10", + "entry_type": "Ion" + }, + { + "@class": "PourbaixEntry", + "@module": "pymatgen.analysis.pourbaix_diagram", + "@version": null, + "concentration": 1e-06, + "entry": { + "energy": -5.82419012875, + "ion": { + "O": 4.0, + 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"pymatgen.analysis.phase_diagram", + "attribute": null, + "composition": { + "O": 1.0, + "Zn": 1.0 + }, + "energy": -2.985511161249999, + "name": "ZnO" + }, + "entry_id": "mp-2229", + "entry_type": "Solid" + }, + { + "@class": "PourbaixEntry", + "@module": "pymatgen.analysis.pourbaix_diagram", + "@version": null, + "concentration": 1.0, + "entry": { + "@class": "PDEntry", + "@module": "pymatgen.analysis.phase_diagram", + "attribute": null, + "composition": { + "O": 1.0, + "Zn": 1.0 + }, + "energy": -3.2654903412500005, + "name": "ZnO" + }, + "entry_id": "mp-1986", + "entry_type": "Solid" + }, + { + "@class": "PourbaixEntry", + "@module": "pymatgen.analysis.pourbaix_diagram", + "@version": null, + "concentration": 1.0, + "entry": { + "@class": "PDEntry", + "@module": "pymatgen.analysis.phase_diagram", + "attribute": null, + "composition": { + "O": 12.0, + "Zn": 12.0 + }, + "energy": -35.20158646499999, + "name": "ZnO" + }, + "entry_id": "mp-997630", + "entry_type": "Solid" + }, + { + "@class": "PourbaixEntry", + "@module": "pymatgen.analysis.pourbaix_diagram", + "@version": null, + "concentration": 1.0, + "entry": { + "@class": "PDEntry", + "@module": "pymatgen.analysis.phase_diagram", + "attribute": null, + "composition": { + "O": 1.0, + "Zn": 1.0 + }, + "energy": -1.8470983312500007, + "name": "ZnO" + }, + "entry_id": "mp-13161", + "entry_type": "Solid" + }, + { + "@class": "PourbaixEntry", + "@module": "pymatgen.analysis.pourbaix_diagram", + "@version": null, + "concentration": 1.0, + "entry": { + "@class": "PDEntry", + "@module": "pymatgen.analysis.phase_diagram", + "attribute": null, + "composition": { + "O": 8.0, + "Zn": 4.0 + }, + "energy": -10.12822242, + "name": "ZnO2" + }, + "entry_id": "mp-8484", + "entry_type": "Solid" + }, + { + "@class": "PourbaixEntry", + "@module": "pymatgen.analysis.pourbaix_diagram", + "@version": null, + "concentration": 1.0, + "entry": { + "@class": "PDEntry", + "@module": "pymatgen.analysis.phase_diagram", + "attribute": null, + "composition": { + "O": 2.0, + "Zn": 2.0 + }, + "energy": -6.5569338725, + "name": "ZnO" + }, + "entry_id": "mp-2133", + "entry_type": "Solid" + }, + { + "@class": "PourbaixEntry", + "@module": "pymatgen.analysis.pourbaix_diagram", + "@version": null, + "concentration": 1.0, + "entry": { + "@class": "PDEntry", + "@module": "pymatgen.analysis.phase_diagram", + "attribute": null, + "composition": { + "O": 2.0, + "Zn": 2.0 + }, + "energy": -5.349318242500001, + "name": "ZnO" + }, + "entry_id": "mp-1017539", + "entry_type": "Solid" + }, + { + "@class": "PourbaixEntry", + "@module": "pymatgen.analysis.pourbaix_diagram", + "@version": null, + "concentration": 1.0, + "entry": { + "@class": "PDEntry", + "@module": "pymatgen.analysis.phase_diagram", + "attribute": null, + "composition": { + "H": 4.0, + "O": 24.0, + "Zn": 24.0 + }, + "energy": -62.32671584447999, + "name": "Zn6HO6" + }, + "entry_id": "mp-985595", + "entry_type": "Solid" + }, + { + "@class": "PourbaixEntry", + "@module": "pymatgen.analysis.pourbaix_diagram", + "@version": null, + "concentration": 1.0, + "entry": { + "@class": "PDEntry", + "@module": "pymatgen.analysis.phase_diagram", + "attribute": null, + "composition": { + "H": 12.0, + "O": 12.0, + "Zn": 6.0 + }, + "energy": -4.280022978440002, + "name": "Zn(HO)2" + }, + "entry_id": "mp-625230", + "entry_type": "Solid" + }, + { + "@class": "PourbaixEntry", + "@module": "pymatgen.analysis.pourbaix_diagram", + "@version": null, + "concentration": 1.0, + "entry": { + "@class": "PDEntry", + "@module": "pymatgen.analysis.phase_diagram", + "attribute": null, + "composition": { + "H": 12.0, + "O": 12.0, + "Zn": 6.0 + }, + "energy": -4.417503758440006, + "name": "Zn(HO)2" + }, + "entry_id": "mp-625272", + "entry_type": "Solid" + }, + { + "@class": "PourbaixEntry", + "@module": "pymatgen.analysis.pourbaix_diagram", + "@version": null, + "concentration": 1.0, + "entry": { + "@class": "PDEntry", + "@module": "pymatgen.analysis.phase_diagram", + "attribute": null, + "composition": { + "H": 12.0, + "O": 12.0, + "Zn": 6.0 + }, + "energy": -4.165272118440015, + "name": "Zn(HO)2" + }, + "entry_id": "mp-625279", + "entry_type": "Solid" + }, + { + "@class": "PourbaixEntry", + "@module": "pymatgen.analysis.pourbaix_diagram", + "@version": null, + "concentration": 1.0, + "entry": { + "@class": "PDEntry", + "@module": "pymatgen.analysis.phase_diagram", + "attribute": null, + "composition": { + "H": 2.0, + "O": 2.0, + "Zn": 1.0 + }, + "energy": -0.5142829397400028, + "name": "Zn(HO)2" + }, + "entry_id": "mp-625487", + "entry_type": "Solid" + }, + { + "@class": "PourbaixEntry", + "@module": "pymatgen.analysis.pourbaix_diagram", + "@version": null, + "concentration": 1.0, + "entry": { + "@class": "PDEntry", + "@module": "pymatgen.analysis.phase_diagram", + "attribute": null, + "composition": { + "H": 2.0, + "O": 2.0, + "Zn": 1.0 + }, + "energy": -0.43221004974000365, + "name": "Zn(HO)2" + }, + "entry_id": "mp-625488", + "entry_type": "Solid" + }, + { + "@class": "PourbaixEntry", + "@module": "pymatgen.analysis.pourbaix_diagram", + "@version": null, + "concentration": 1.0, + "entry": { + "@class": "PDEntry", + "@module": "pymatgen.analysis.phase_diagram", + "attribute": null, + "composition": { + "H": 8.0, + "O": 8.0, + "Zn": 4.0 + }, + "energy": -2.0932910189600022, + "name": "Zn(HO)2" + }, + "entry_id": "mp-625826", + "entry_type": "Solid" + }, + { + "@class": "PourbaixEntry", + "@module": "pymatgen.analysis.pourbaix_diagram", + "@version": null, + "concentration": 1.0, + "entry": { + "@class": "PDEntry", + "@module": "pymatgen.analysis.phase_diagram", + "attribute": null, + "composition": { + "H": 8.0, + "O": 8.0, + "Zn": 4.0 + }, + "energy": -3.7370856289600027, + "name": "Zn(HO)2" + }, + "entry_id": "mp-625830", + "entry_type": "Solid" + }, + { + "@class": "PourbaixEntry", + "@module": "pymatgen.analysis.pourbaix_diagram", + "@version": null, + "concentration": 1.0, + "entry": { + "@class": "PDEntry", + "@module": "pymatgen.analysis.phase_diagram", + "attribute": null, + "composition": { + "H": 8.0, + "O": 8.0, + "Zn": 4.0 + }, + "energy": -3.7015801789600147, + "name": "Zn(HO)2" + }, + "entry_id": "mp-625857", + "entry_type": "Solid" + }, + { + "@class": "PourbaixEntry", + "@module": "pymatgen.analysis.pourbaix_diagram", + "@version": null, + "concentration": 1.0, + "entry": { + "@class": "PDEntry", + "@module": "pymatgen.analysis.phase_diagram", + "attribute": null, + "composition": { + "H": 8.0, + "O": 8.0, + "Zn": 4.0 + }, + "energy": -3.4376028389600037, + "name": "Zn(HO)2" + }, + "entry_id": "mp-697146", + "entry_type": "Solid" + } + ] +} diff --git a/tests/test_pourbaix_api.py b/tests/test_pourbaix_api.py new file mode 100644 index 00000000..c67c5dfc --- /dev/null +++ b/tests/test_pourbaix_api.py @@ -0,0 +1,113 @@ +import itertools +import os + +import numpy as np +import pytest + +pytest.importorskip("mp_api", reason="mp_api not installed or incompatible with this Python version") +from mp_api.client import MPRester +from pymatgen.analysis.phase_diagram import PhaseDiagram +from pymatgen.analysis.pourbaix_diagram import IonEntry, PourbaixDiagram, PourbaixEntry +from pymatgen.core.ion import Ion +from pymatgen.entries.compatibility import MaterialsProjectAqueousCompatibility + +from pyEQL.pourbaix.pourbaix_api import Pourbaix_api + + +@pytest.fixture +def mpr(): + rester = MPRester() + yield rester + rester.session.close() + + +@pytest.mark.skipif(os.getenv("MP_API_KEY", None) is None, reason="No API key found.") +class TestMPRester: + fake_mp_api_key = "12345678901234567890123456789012" + default_endpoint = "https://api.materialsproject.org/" + + @pytest.mark.skip(reason="SSL issues") + def test_get_ion_entries(self, mpr): + entries = mpr.get_entries_in_chemsys("Ti-O-H") + pd = PhaseDiagram(entries) + pourbaix_api = Pourbaix_api(mpr) # instantiated in test + ion_entry_data = pourbaix_api.get_ion_reference_data_for_chemsys("Ti-O-H") + ion_entries = pourbaix_api.get_ion_entries(pd, ion_entry_data) + assert len(ion_entries) == 5 + assert all(isinstance(i, IonEntry) for i in ion_entries) + bi_v_entry_data = pourbaix_api.get_ion_reference_data_for_chemsys("Bi-V") + bi_data = pourbaix_api.get_ion_reference_data_for_chemsys("Bi") + v_data = pourbaix_api.get_ion_reference_data_for_chemsys("V") + assert len(bi_v_entry_data) == len(bi_data) + len(v_data) + + # test an incomplete phase diagram + entries = mpr.get_entries_in_chemsys("Ti-O") + pd = PhaseDiagram(entries) + with pytest.raises(ValueError, match="The phase diagram chemical system"): + pourbaix_api.get_ion_entries(pd) + + # test ion energy calculation + ion_data = pourbaix_api.get_ion_reference_data_for_chemsys("S") + ion_ref_comps = [Ion.from_formula(d["data"]["RefSolid"]).composition for d in ion_data] + ion_ref_elts = set(itertools.chain.from_iterable(i.elements for i in ion_ref_comps)) + ion_ref_entries = mpr.get_entries_in_chemsys([*map(str, ion_ref_elts), "O", "H"]) + mpc = MaterialsProjectAqueousCompatibility() + ion_ref_entries = mpc.process_entries(ion_ref_entries) + ion_ref_pd = PhaseDiagram(ion_ref_entries) + ion_entries = pourbaix_api.get_ion_entries(ion_ref_pd, ion_ref_data=ion_data) + + # In ion ref data, SO4-2 is -744.27 kJ/mol; ref solid is -1,279.0 kJ/mol + # so the ion entry should have an energy (-744.27 +1279) = 534.73 kJ/mol + # or 5.542 eV/f.u. above the energy of Na2SO4 + so4_two_minus = next(e for e in ion_entries if e.ion.reduced_formula == "SO4[-2]") + + # the ref solid is Na2SO4, ground state mp-4770 + # the rf factor correction is necessary to make sure the composition + # of the reference solid is normalized to a single formula unit + ref_solid_entry = next(e for e in ion_ref_entries if e.entry_id == "mp-4770-GGA") + rf = ref_solid_entry.composition.get_reduced_composition_and_factor()[1] + solid_energy = ion_ref_pd.get_form_energy(ref_solid_entry) / rf + + assert np.allclose(so4_two_minus.energy, solid_energy + 5.542, atol=1e-3) + + @pytest.mark.skip(reason="SSL issues") + def test_get_pourbaix_entries(self, mpr): + # test input chemsys as a list of elements + pourbaix_api = Pourbaix_api(mpr) # instantiated in test + pbx_entries = pourbaix_api.get_pourbaix_entries(["Fe", "Cr"]) + for pbx_entry in pbx_entries: + assert isinstance(pbx_entry, PourbaixEntry) + + # test input chemsys as a string + pbx_entries = pourbaix_api.get_pourbaix_entries("Fe-Cr") + for pbx_entry in pbx_entries: + assert isinstance(pbx_entry, PourbaixEntry) + + # test use_gibbs kwarg + pbx_entries = pourbaix_api.get_pourbaix_entries("Li-O", use_gibbs=300) + for pbx_entry in pbx_entries: + assert isinstance(pbx_entry, PourbaixEntry) + + # test solid_compat kwarg + with pytest.raises(ValueError, match="Solid compatibility can only be"): + pourbaix_api.get_pourbaix_entries("Ti-O", solid_compat=None) + + # test removal of extra elements from reference solids + # Li-Zn-S has Na in reference solids + pbx_entries = pourbaix_api.get_pourbaix_entries("Li-Zn-S") + assert not any(e for e in pbx_entries if "Na" in e.composition) + + # Ensure entries are pourbaix compatible + PourbaixDiagram(pbx_entries) + + # TODO - old tests copied from pymatgen with specific energy values. Update or delete + # fe_two_plus = [e for e in pbx_entries if e.entry_id == "ion-0"][0] + # self.assertAlmostEqual(fe_two_plus.energy, -1.12369, places=3) + # + # feo2 = [e for e in pbx_entries if e.entry_id == "mp-25332"][0] + # self.assertAlmostEqual(feo2.energy, 3.56356, places=3) + # + # # Test S, which has Na in reference solids + # pbx_entries = self.rester.get_pourbaix_entries(["S"]) + # so4_two_minus = pbx_entries[9] + # self.assertAlmostEqual(so4_two_minus.energy, 0.301511, places=3) diff --git a/tests/test_pourbaix_diagram.py b/tests/test_pourbaix_diagram.py new file mode 100644 index 00000000..2d51403a --- /dev/null +++ b/tests/test_pourbaix_diagram.py @@ -0,0 +1,373 @@ +from __future__ import annotations + +import multiprocessing +from importlib.resources import files +from unittest import TestCase + +import matplotlib.pyplot as plt +import numpy as np +from monty.serialization import dumpfn, loadfn +from pymatgen.core.composition import Composition +from pymatgen.entries.computed_entries import ComputedEntry +from pymatgen.util.testing import PymatgenTest +from pytest import approx + +from pyEQL import Solution +from pyEQL.pourbaix.ion import Ion +from pyEQL.pourbaix.pourbaix_diagram import ( + IonEntry, + MultiEntry, + PourbaixDiagram, + PourbaixEntry, + PourbaixPlotter, + ion_or_solid_comp_object, +) + +TEST_DIR = "." + + +class TestPourbaixEntry(PymatgenTest): + """Test all functions using a fictitious entry""" + + def setUp(self): + # comp = Composition("Mn2O3") + self.sol_entry = ComputedEntry("Mn2O3", 49) + ion = Ion.from_formula("MnO4-") + self.ion_entry = IonEntry(ion, 25) + self.px_ion = PourbaixEntry(self.ion_entry) + self.px_sol = PourbaixEntry(self.sol_entry) + self.px_ion.concentration = 1e-4 + + def test_pourbaix_entry(self): + assert self.px_ion.entry.energy == 25, "Wrong Energy!" + assert self.px_ion.entry.name == "MnO4[-1]", "Wrong Entry!" + assert self.px_sol.entry.energy == 49, "Wrong Energy!" + assert self.px_sol.entry.name == "Mn2O3", "Wrong Entry!" + # assert self.PxIon.energy == 25, "Wrong Energy!" + # assert self.PxSol.energy == 49, "Wrong Energy!" + assert self.px_ion.concentration == approx(1e-4), "Wrong concentration!" + + def test_calc_coeff_terms(self): + assert self.px_ion.npH == -8, "Wrong npH!" + assert self.px_ion.nPhi == -7, "Wrong nPhi!" + assert self.px_ion.nH2O == 4, "Wrong nH2O!" + + assert self.px_sol.npH == -6, "Wrong npH!" + assert self.px_sol.nPhi == -6, "Wrong nPhi!" + assert self.px_sol.nH2O == 3, "Wrong nH2O!" + + def test_as_from_dict(self): + dct = self.px_ion.as_dict() + ion_entry = self.px_ion.from_dict(dct) + assert ion_entry.entry.name == "MnO4[-1]", "Wrong Entry!" + + dct = self.px_sol.as_dict() + sol_entry = self.px_sol.from_dict(dct) + assert sol_entry.name == "Mn2O3(s)", "Wrong Entry!" + assert sol_entry.energy == self.px_sol.energy, "as_dict and from_dict energies unequal" + + # Ensure computed entry data persists + entry = ComputedEntry("TiO2", energy=-20, data={"test": "test"}) + pbx_entry = PourbaixEntry(entry=entry) + dumpfn(pbx_entry, "pbx_entry.json") + reloaded = loadfn("pbx_entry.json") + assert isinstance(reloaded.entry, ComputedEntry) + assert reloaded.entry.data is not None + + def test_energy_functions(self): + # TODO: test these for values + self.px_sol.energy_at_conditions(10, 0) + self.px_sol.energy_at_conditions(np.array([1, 2, 3]), 0) + self.px_sol.energy_at_conditions(10, np.array([1, 2, 3])) + self.px_sol.energy_at_conditions(np.array([1, 2, 3]), np.array([1, 2, 3])) + + def test_multi_entry(self): + # TODO: More robust multi-entry test + m_entry = MultiEntry([self.px_sol, self.px_ion]) + for attr in ["energy", "composition", "nPhi"]: + assert getattr(m_entry, attr) == getattr(self.px_sol, attr) + getattr(self.px_ion, attr) + + # As dict, from dict + m_entry_dict = m_entry.as_dict() + m_entry_new = MultiEntry.from_dict(m_entry_dict) + assert m_entry_new.energy == m_entry.energy + + def test_multi_entry_repr(self): + m_entry = MultiEntry([self.px_sol, self.px_ion]) + assert ( + repr(m_entry) == "PourbaixMultiEntry(energy=90.9717, npH=-14.0, nPhi=-13.0, nH2O=7.0, " + "entry_id=[None, None], species='Mn2O3(s) + MnO4[-1]')" + ) + + def test_get_elt_fraction(self): + entry = ComputedEntry("Mn2Fe3O3", 49) + pb_entry = PourbaixEntry(entry) + assert pb_entry.get_element_fraction("Fe") == approx(0.6) + assert pb_entry.get_element_fraction("Mn") == approx(0.4) + + +class TestPourbaixDiagram(TestCase): + @classmethod + def setUpClass(cls): + # cls.test_data = loadfn(f"{TEST_DIR}/pourbaix_test_data.json") + pourbaix_test_data = files("pyEQL") / "pourbaix" / "pourbaix_test_data.json" + pourbaix_test_dir = str(pourbaix_test_data) + cls.test_data = loadfn(pourbaix_test_dir) + cls.pbx = PourbaixDiagram(cls.test_data["Zn"], filter_solids=True) + cls.pbx_no_filter = PourbaixDiagram(cls.test_data["Zn"], filter_solids=False) + + def test_pourbaix_diagram(self): + assert {entry.name for entry in self.pbx.stable_entries} == { + "ZnO(s)", + "Zn[2+]", + "ZnHO2[-]", + "ZnO2[2-]", + "Zn(s)", + }, "List of stable entries does not match" + + assert {entry.name for entry in self.pbx_no_filter.stable_entries} == { + "ZnO(s)", + "Zn[2+]", + "ZnHO2[-]", + "ZnO2[2-]", + "Zn(s)", + "ZnO2(s)", + "ZnH(s)", + }, "List of stable entries for unfiltered pbx does not match" + + pbx_low_conc = PourbaixDiagram(self.test_data["Zn"], conc_dict={"Zn": 1e-8}, filter_solids=True) + assert {entry.name for entry in pbx_low_conc.stable_entries} == { + "Zn(HO)2(aq)", + "Zn[2+]", + "ZnHO2[-]", + "ZnO2[2-]", + "Zn(s)", + } + + def test_properties(self): + assert len(self.pbx.unstable_entries) == 2 + + def test_multicomponent(self): + # Assure no ions get filtered at high concentration + ag_n = [entry for entry in self.test_data["Ag-Te-N"] if "Te" not in entry.composition] + highconc = PourbaixDiagram(ag_n, filter_solids=True, conc_dict={"Ag": 1e-5, "N": 1}) + entry_sets = [set(entry.entry_id) for entry in highconc.stable_entries] + assert {"mp-124", "ion-17"} in entry_sets + + # Binary system + pd_binary = PourbaixDiagram( + self.test_data["Ag-Te"], + filter_solids=True, + comp_dict={"Ag": 0.5, "Te": 0.5}, + conc_dict={"Ag": 1e-8, "Te": 1e-8}, + ) + assert len(pd_binary.stable_entries) == 30 + test_entry = pd_binary.find_stable_entry(8, 2) + assert "mp-499" in test_entry.entry_id + + # Find a specific multi-entry to test + assert pd_binary.get_decomposition_energy(test_entry, 8, 2) == 0 + + pd_ternary = PourbaixDiagram(self.test_data["Ag-Te-N"], filter_solids=True) + assert len(pd_ternary.stable_entries) == 49 + + # Fetch a solid entry and a ground state entry mixture + ag_te_n = self.test_data["Ag-Te-N"][-1] + ground_state_ag_with_ions = MultiEntry( + [self.test_data["Ag-Te-N"][i] for i in [4, 18, 30]], + weights=[1 / 3, 1 / 3, 1 / 3], + ) + assert pd_ternary.get_decomposition_energy(ag_te_n, 2, -1) == approx(2.767822855765) + assert pd_ternary.get_decomposition_energy(ag_te_n, 10, -2) == approx(3.756840056890625) + assert pd_ternary.get_decomposition_energy(ground_state_ag_with_ions, 2, -1) == approx(0) + + # Test invocation of Pourbaix diagram from ternary data + new_ternary = PourbaixDiagram(pd_ternary.all_entries) + assert len(new_ternary.stable_entries) == 49 + assert new_ternary.get_decomposition_energy(ag_te_n, 2, -1) == approx(2.767822855765) + assert new_ternary.get_decomposition_energy(ag_te_n, 10, -2) == approx(3.756840056890625) + assert new_ternary.get_decomposition_energy(ground_state_ag_with_ions, 2, -1) == approx(0) + + # Test processing of multi-entries with degenerate reaction, produced + # a bug in a prior implementation + entries = [ + PourbaixEntry(ComputedEntry("VFe2Si", -1.8542253150000008), entry_id="mp-4595"), + PourbaixEntry(ComputedEntry("Fe", 0), entry_id="mp-13"), + PourbaixEntry(ComputedEntry("V2Ir2", -2.141851640000006), entry_id="mp-569250"), + PourbaixEntry( + IonEntry(Ion.from_formula("Fe[2+]"), -0.7683100214319288), + entry_id="ion-0", + ), + PourbaixEntry( + IonEntry(Ion.from_formula("Li[1+]"), -3.0697590542787156), + entry_id="ion-12", + ), + ] + comp_dict = Composition({"Fe": 1, "Ir": 1, "Li": 2, "Si": 1, "V": 2}).fractional_composition + + multi_entry = PourbaixDiagram.process_multientry(entries, prod_comp=comp_dict) + assert multi_entry is None + + def test_get_pourbaix_domains(self): + domains = PourbaixDiagram.get_pourbaix_domains(self.test_data["Zn"]) + assert len(domains[0]) == 7 + + def test_get_decomposition(self): + # Test a stable entry to ensure that it's zero in the stable region + entry = self.test_data["Zn"][12] # Should correspond to mp-2133 + assert self.pbx.get_decomposition_energy(entry, 10, 1) == approx(0.0, 5), ( + "Decomposition energy of ZnO is not 0." + ) + + # Test an unstable entry to ensure that it's never zero + entry = self.test_data["Zn"][11] + ph, v = np.meshgrid(np.linspace(0, 14), np.linspace(-2, 4)) + result = self.pbx_no_filter.get_decomposition_energy(entry, ph, v) + assert (result >= 0).all(), "Unstable energy has hull energy of 0 or less" + + # Test an unstable hydride to ensure HER correction works + assert self.pbx.get_decomposition_energy(entry, -3, -2) == approx(3.6979147983333) + # Test a list of pHs + self.pbx.get_decomposition_energy(entry, np.linspace(0, 2, 5), 2) + + # Test a list of Vs + self.pbx.get_decomposition_energy(entry, 4, np.linspace(-3, 3, 10)) + + # Test a set of matching arrays + ph, v = np.meshgrid(np.linspace(0, 14), np.linspace(-3, 3)) + self.pbx.get_decomposition_energy(entry, ph, v) + + # Test custom ions + entries = self.test_data["C-Na-Sn"] + ion = IonEntry(Ion.from_formula("NaO28H80Sn12C24+"), -161.676) + custom_ion_entry = PourbaixEntry(ion, entry_id="some_ion") + pbx = PourbaixDiagram( + [*entries, custom_ion_entry], + filter_solids=True, + comp_dict={"Na": 1, "Sn": 12, "C": 24}, + ) + assert pbx.get_decomposition_energy(custom_ion_entry, 5, 2) == approx(2.1209002582, abs=1e-1) + + def test_get_stable_entry(self): + entry = self.pbx.get_stable_entry(0, 0) + assert entry.entry_id == "ion-0" + + def test_multielement_parallel(self): + # Simple test to ensure that multiprocessing is working + test_entries = self.test_data["Ag-Te-N"] + nproc = multiprocessing.cpu_count() + pbx = PourbaixDiagram(test_entries, filter_solids=True, nproc=nproc) + assert len(pbx.stable_entries) == 49 + + def test_solid_filter(self): + entries = self.test_data["Zn"] + pbx = PourbaixDiagram(entries, filter_solids=False) + oxidized_phase = pbx.find_stable_entry(10, 2) + assert oxidized_phase.name == "ZnO2(s)" + + entries = self.test_data["Zn"] + pbx = PourbaixDiagram(entries, filter_solids=True) + oxidized_phase = pbx.find_stable_entry(10, 2) + assert oxidized_phase.name == "ZnO(s)" + + def test_serialization(self): + dct = self.pbx.as_dict() + new = PourbaixDiagram.from_dict(dct) + assert {entry.name for entry in new.stable_entries} == { + "ZnO(s)", + "Zn[2+]", + "ZnHO2[-]", + "ZnO2[2-]", + "Zn(s)", + }, "List of stable entries does not match" + + # Test with unstable solid entries included (filter_solids=False), this should result in the + # previously filtered entries being included + dct = self.pbx_no_filter.as_dict() + new = PourbaixDiagram.from_dict(dct) + assert {entry.name for entry in new.stable_entries} == { + "ZnO(s)", + "Zn[2+]", + "ZnHO2[-]", + "ZnO2[2-]", + "Zn(s)", + "ZnO2(s)", + "ZnH(s)", + }, "List of stable entries for unfiltered pbx does not match" + + pd_binary = PourbaixDiagram( + self.test_data["Ag-Te"], + filter_solids=True, + comp_dict={"Ag": 0.5, "Te": 0.5}, + conc_dict={"Ag": 1e-8, "Te": 1e-8}, + ) + new_binary = PourbaixDiagram.from_dict(pd_binary.as_dict()) + assert len(pd_binary.stable_entries) == len(new_binary.stable_entries) + + def test_speciate_comp_dict(self): + default_pH = 7 + default_units = "mol/L" + self.comp_dict = {"Ca": 1, "Mg": 1, "Cl": 2, "SO4": 1} + ion_dict = {ion: f"{amount} {default_units}" for ion, amount in self.comp_dict.items()} + test_sol = Solution(ion_dict, pH=default_pH) + assert np.isclose(test_sol.get_amount("Ca", default_units).magnitude, 1) + assert np.isclose(test_sol.get_amount("Mg", default_units).magnitude, 1) + assert np.isclose(test_sol.get_amount("Cl", default_units).magnitude, 2) + assert np.isclose(test_sol.get_amount("SO4", default_units).magnitude, 1) + + +class TestPourbaixPlotter(TestCase): + def setUp(self): + pourbaix_test_data = files("pyEQL") / "pourbaix" / "pourbaix_test_data.json" + pourbaix_test_dir = str(pourbaix_test_data) + self.test_data = loadfn(pourbaix_test_dir) + self.pd = PourbaixDiagram(self.test_data["Zn"]) + self.plotter = PourbaixPlotter(self.pd) + + def test_plot_pourbaix(self): + plotter = PourbaixPlotter(self.pd) + # Default limits + plotter.get_pourbaix_plot() + # Non-standard limits + plotter.get_pourbaix_plot(limits=[[-5, 4], [-2, 2]]) + + def test_plot_entry_stability(self): + entry = self.pd.all_entries[0] + self.plotter.plot_entry_stability(entry, limits=[[-2, 14], [-3, 3]]) + + # binary system + pd_binary = PourbaixDiagram(self.test_data["Ag-Te"], comp_dict={"Ag": 0.5, "Te": 0.5}) + binary_plotter = PourbaixPlotter(pd_binary) + ax = binary_plotter.plot_entry_stability(self.test_data["Ag-Te"][53]) + assert isinstance(ax, plt.Axes) + + +class TestIonOrSolidCompObject: + def test_ion(self): + # Test cations + assert ion_or_solid_comp_object("Li+").charge == 1 + assert ion_or_solid_comp_object("Li[+]").charge == 1 + assert ion_or_solid_comp_object("Ca[2+]").charge == 2 + assert ion_or_solid_comp_object("Ca[+2]").charge == 2 + assert ion_or_solid_comp_object("Ca++").charge == 2 + assert ion_or_solid_comp_object("Ca[++]").charge == 2 + assert ion_or_solid_comp_object("Ca2+").charge == 1 + assert ion_or_solid_comp_object("C2O4-2").charge == -2 + + # Test anions + assert ion_or_solid_comp_object("Cl-").charge == -1 + assert ion_or_solid_comp_object("Cl[-]").charge == -1 + assert ion_or_solid_comp_object("SO4[-2]").charge == -2 + assert ion_or_solid_comp_object("SO4-2").charge == -2 + assert ion_or_solid_comp_object("SO42-").charge == -1 + assert ion_or_solid_comp_object("SO4--").charge == -2 + assert ion_or_solid_comp_object("SO4[--]").charge == -2 + assert ion_or_solid_comp_object("N3-").charge == -1 + + def test_solid(self): + # Test end with "(s)" + assert ion_or_solid_comp_object("Fe2O3(s)") == Composition("Fe2O3") + assert ion_or_solid_comp_object("Fe(s)") == Composition("Fe1") + + # Test end without "(s)" + assert type(ion_or_solid_comp_object("Na2O")) is Composition