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‎.github/workflows/deploy.yml‎

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- uses: actions/setup-python@v5
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with:
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python-version: "3.10"
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python-version: "3.11"
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cache: pip
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cache-dependency-path: pyproject.toml
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- uses: actions/setup-python@v5
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with:
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python-version: "3.10"
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- name: Build
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‎.github/workflows/docs.yml‎

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- uses: actions/setup-python@v5
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python-version: "3.10"
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python-version: "3.11"
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cache: pip
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cache-dependency-path: pyproject.toml
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‎.github/workflows/testing.yml‎

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- uses: actions/setup-python@v5
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python-version: "3.11"
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cache: pip
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cache-dependency-path: pyproject.toml
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shell: bash -l {0} # enables conda/mamba env activation by reading bash profile
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strategy:
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matrix:
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python-version: ["3.10", "3.11", "3.12"]
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python-version: ["3.11", "3.12"]
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split: [1, 2, 3]
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steps:
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- name: Set up micromamba
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uses: mamba-org/setup-micromamba@main
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- name: Create mamba environment
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run: |
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micromamba create -n a2 python=${{ matrix.python-version }} --yes
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with:
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environment-name: a2
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cache-environment: false
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create-args: >-
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python=${{ matrix.python-version }}
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- name: Install uv
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run: micromamba run -n a2 pip install uv
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- uses: codecov/codecov-action@v1
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if: matrix.python-version == '3.10' && github.repository == 'materialsproject/atomate2'
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if: matrix.python-version == '3.11' && github.repository == 'materialsproject/atomate2'
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with:
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token: ${{ secrets.CODECOV_TOKEN }}
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name: coverage${{ matrix.split }}
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shell: bash -l {0} # enables conda/mamba env activation by reading bash profile
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strategy:
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matrix:
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python-version: ["3.10","3.11","3.12"]
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python-version: ["3.11","3.12"]
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steps:
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- name: Check out repo
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uses: actions/checkout@v4
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- name: Set up micromamba
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uses: mamba-org/setup-micromamba@main
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- name: Create mamba environment
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run: |
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micromamba create -n a2 python=${{ matrix.python-version }} --yes
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with:
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environment-name: a2
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cache-environment: false
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create-args: >-
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python=${{ matrix.python-version }}
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- name: Install uv
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run: micromamba run -n a2 pip install uv
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shell: bash -l {0} # enables conda/mamba env activation by reading bash profile
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strategy:
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matrix:
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python-version: ["3.10", "3.11", "3.12"]
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python-version: ["3.11", "3.12"]
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steps:
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- name: Check out repo
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uses: actions/checkout@v4
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- name: Set up micromamba
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uses: mamba-org/setup-micromamba@main
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- name: Create mamba environment
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run: |
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micromamba create -n a2 python=${{ matrix.python-version }} --yes
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with:
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environment-name: a2
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cache-environment: false
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create-args: >-
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python=${{ matrix.python-version }}
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- name: Install uv
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run: micromamba run -n a2 pip install uv
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pytest -n auto --splits 1 --group 1 --cov=atomate2 --cov-report=xml tests/ase
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- uses: codecov/codecov-action@v1
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if: matrix.python-version == '3.10' && github.repository == 'materialsproject/atomate2'
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if: matrix.python-version == '3.11' && github.repository == 'materialsproject/atomate2'
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with:
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token: ${{ secrets.CODECOV_TOKEN }}
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file: ./coverage.xml
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shell: bash -l {0} # enables conda/mamba env activation by reading bash profile
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strategy:
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python-version: ["3.10", "3.11", "3.12"]
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python-version: ["3.11", "3.12"]
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steps:
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- name: Check out repo
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uses: actions/checkout@v4
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- name: Set up micromamba
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uses: mamba-org/setup-micromamba@main
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- name: Create mamba environment
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run: |
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micromamba create -n a2 python=${{ matrix.python-version }} --yes
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with:
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environment-name: a2
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cache-environment: false
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create-args: >-
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python=${{ matrix.python-version }}
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- name: Install uv
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run: micromamba run -n a2 pip install uv
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- uses: codecov/codecov-action@v1
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if: matrix.python-version == '3.10' && github.repository == 'materialsproject/atomate2'
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if: matrix.python-version == '3.11' && github.repository == 'materialsproject/atomate2'
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token: ${{ secrets.CODECOV_TOKEN }}
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name: coverage
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python-version: "3.11"
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cache: pip
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‎.github/workflows/update-precommit.yml‎

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- name: Set up Python
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uses: actions/setup-python@v5
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python-version: "3.10"
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- name: Install pre-commit
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‎.gitignore‎

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docs/tutorials/*
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docs/tutorials/*/*
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# tutorial output files
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tutorials/*.json.gz
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# C extensions
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*.so
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.ipynb_checkpoints
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.aider*
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# deepmd-kit files
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**/*.pb

‎docs/user/codes/forcefields.md‎

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(codes.forcefields)=
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# Machine Learning forcefields / interatomic potentials
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`atomate2` includes an interface to a few common machine learning interatomic potentials (MLIPs), also known variously as machine learning forcefields (MLFFs), or foundation potentials (FPs) for universal variants.
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Support is provided for the following models, which can be selected using `atomate2.forcefields.utils.MLFF`, as shown in the table below.
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**You need only install packages for the forcefields you wish to use.**
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| Forcefield Name | `MLFF` | Reference | Description |
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| ---- | ---- | ---- | ---- |
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| CHGNet | `CHGNet` | [10.1038/s42256-023-00716-3](https://doi.org/10.1038/s42256-023-00716-3) | Available via the `chgnet` and `matgl` packages |
12+
| DeepMD | `MLFF.DeepMD` | [10.1103/PhysRevB.108.L180104](https://doi.org/10.1103/PhysRevB.108.L180104) | The Deep Potential model used for this test is `UniPero`, a universal interatomic potential for perovskite oxides. It can be downloaded [here](https://github.com/sliutheorygroup/UniPero) |
13+
| Gaussian Approximation Potential (GAP) | `GAP` | [10.1103/PhysRevLett.104.136403](https://doi.org/10.1103/PhysRevLett.104.136403) | Relies on `quippy-ase` package |
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| M3GNet | `M3GNet` | [10.1038/s43588-022-00349-3](https://doi.org/10.1038/s43588-022-00349-3) | Relies on `matgl` package |
15+
| MACE-MP-0 | `MACE` or `MACE_MP_0` (recommended) | [10.1063/5.0297006](https://doi.org/10.1063/5.0297006) | Relies on `mace_torch` and optionally `torch_dftd` packages |
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| MACE-MP-0b3 | `MACE_MP_0B3` | [10.1063/5.0297006](https://doi.org/10.1063/5.0297006) | Relies on `mace_torch` and optionally `torch_dftd` packages |
17+
| MACE-MPA-0 | `MACE_MPA_0` | [10.1063/5.0297006](https://doi.org/10.1063/5.0297006) | Relies on `mace_torch` and optionally `torch_dftd` packages |
18+
| MatPES-PBE | `MATPES_PBE` | [10.48550/arXiv.2503.04070](https://doi.org/10.48550/arXiv.2503.04070) | Relies on `matgl`. Defaults to TensorNet architecture, but can also use M3GNet or CHGNet architectures via kwargs. See `atomate2.forcefields.utils._DEFAULT_CALCULATOR_KWARGS` for more options. |
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| MatPES-r<sup>2</sup>SCAN | `MATPES_R2SCAN`| [10.48550/arXiv.2503.04070](https://doi.org/10.48550/arXiv.2503.04070) | Relies on `matgl`. Defaults to TensorNet architecture, but can also use M3GNet or CHGNet architectures via kwargs. See `atomate2.forcefields.utils._DEFAULT_CALCULATOR_KWARGS` for more options. |
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| Neuroevolution Potential (NEP) | `NEP` | [10.1103/PhysRevB.104.104309](https://doi.org/10.1103/PhysRevB.104.104309) | Relies on `calorine` package |
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| Neural Equivariant Interatomic Potentials (Nequip) | `Nequip` | [10.1038/s41467-022-29939-5](https://doi.org/10.1038/s41467-022-29939-5) | Relies on the `nequip` package |
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| SevenNet | `SevenNet` | [10.1021/acs.jctc.4c00190](https://doi.org/10.1021/acs.jctc.4c00190) | Relies on the `sevenn` package |

‎docs/user/codes/index.md‎

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```{toctree}
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vasp
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openmm
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forcefields
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```

‎docs/user/install.md‎

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### Materials Project API key
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You can get an API key from the [Materials Project] by logging in and going to your
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[Dashboard](materials project). Add this also to
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your `~/.config/.pmgrc.yaml` so that it looks like the following
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[Dashboard](materials project).
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```yaml
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PMG_VASP_PSP_DIR: <<INSTALL_DIR>>/pps
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PMG_MAPI_KEY: <<YOUR_API_KEY>>
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Add this to your `.bashrc`, `.zshrc`, etc. as an environment variable:
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```console
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export MP_API_KEY=your_api_key_here
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```
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You can generate this file and set these values using the `pymatgen` CLI:
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## Emmet-core setup
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```bash
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pmg config --add PMG_VASP_PSP_DIR /abs/path/to/psp PMG_MAPI_KEY your_api_key
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The `emmet-core` package is used to define data schemas for parsing outputs of workflows.
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It is also used in building the Materials Project data, therefore its use in `atomate2` is to broadly ensure compatibility with the Materials Project's data structures.
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`emmet-core` allows you to use either `pymatgen` or `emmet-core`-defined models for larger data objects, such as charge densities (`CHGCAR`, `AECCAR*`), or trajectories (relaxation, MD, etc.).
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The `pymatgen` objects have long been the default in workflows, and are structured to be output as JSON files.
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The `emmet-core` objects have been designed with both JSON and Apache parquet as storage formats.
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If you will be storing data in the cloud, or would like to use these newer data models which may use less storage, you can either add a line to your `atomate2.yaml` file:
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```yaml
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VASP_USE_EMMET_MODELS: true
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```
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or use an environment variable in your `.bashrc`:
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```console
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export ATOMATE2_VASP_USE_EMMET_MODEL=true
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```
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Note that there is an equivalent `emmet-core` setting, which can be set by the environment variable `EMMET_USE_EMMET_MODELS`.
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For ASE and machine learning forcefield jobs, you can use the `ASE_FORCEFIELD_USE_EMMET_MODELS` flag in `atomate2.yaml` to toggle the same functionality.
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The default in `atomate2` is to use `pymatgen` models.
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‎pyproject.toml‎

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"click",
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"custodian>=2024.4.18",
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"emmet-core>=v0.84.10",
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"emmet-core>=0.86.1",
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"jobflow>=0.1.11",
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"monty>=2024.12.10",
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"numpy",
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"chgnet>=0.2.2",
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"mace-torch>=0.3.3",
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"matgl>=1.2.1",
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"torchdata<=0.7.1",
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"torchdata<=0.7.1", # TODO: remove when issue fixed
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# quippy-ase support for py3.12 tracked in https://github.com/libAtoms/QUIP/issues/645
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"quippy-ase>=0.9.14; python_version < '3.12'",
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"torchdata<=0.7.1", # TODO: remove when issue fixed
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"deepmd-kit>=2.1.4",
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torchsim = [
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"sphinx-copybutton==0.5.2",
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"sphinx==8.1.3",
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"sphinx_design==0.6.1",
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"jupyterlab==4.4.10",
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"jupyterlab==4.5.0",
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dev = ["pre-commit>=2.12.1"]
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tests = [
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"mdanalysis==2.9.0",
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"monty==2025.3.3",
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"openmm-mdanalysis-reporter==0.1.0",
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"openmm==8.4.0",
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"openmm==8.4.0.post2",
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"pymatgen==2025.10.7", # EXERCISE CAUTION WHEN UPDATING - open ff is extremely sensitive to pymatgen version
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]
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strict-forcefields = [
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"calorine==3.2; python_version >= '3.12'",
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"calorine==3.1; python_version < '3.12'",
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"chgnet==0.3.8",
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"mace-torch==0.3.14",
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"matgl==1.3.0",
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"matgl==2.0.4",
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"quippy-ase==0.9.14; python_version < '3.12'",
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"sevenn==0.10.4",
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"torch==2.2.0",
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"torchdata==0.7.1", # TODO: remove when issue fixed
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"deepmd-kit==2.2.11",
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"tensorflow-cpu==2.16.2",
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[project.scripts]

‎src/atomate2/abinit/schemas/calculation.py‎

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from abipy.flowtk import events
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from emmet.core.math import Matrix3D, Vector3D
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from emmet.core.types.enums import ValueEnum
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except ImportError:
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from emmet.core.utils import ValueEnum
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from emmet.core.types.enums import ValueEnum
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from pydantic import BaseModel, Field
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from pymatgen.core import Structure
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from typing_extensions import Self

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