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README.md

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@@ -12,6 +12,7 @@ The Python package is named as `ppafm`.
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## Documentation
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* [Wiki](https://github.com/Probe-Particle/ProbeParticleModel/wiki)
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* [readthedocs](https://ppafm.readthedocs.io/en/latest/?badge=latest)
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* Auto-generated:
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* [DeepWiki](https://deepwiki.com/Probe-Particle/ppafm)
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pip install ppafm
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```
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This should install the package and all its dependencies.
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This should install the package and all its dependencies including **CPU** and **GPU** version and fully working CLI:
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The most up-to-date installation guide can be found on the [dedicated wiki page](https://github.com/Probe-Particle/ppafm/wiki/Install-ppafm).
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* `examples/Generator` quickly generates a batch of simulated AFM images (resp. 3D data stacks) which can be further used for machine learning.
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Especially in connection with (https://github.com/SINGROUP/ASD-AFM).
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## ppafm simulation models and implementations
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Since 2014 ppafm developed into the toolbox of various methodologies adjusted for a particular use case.
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1. **CPU version:** - Original implementation using Python & C/C++.
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It can simulate a typical AFM experiment (3D stack of AFM images) in a matter of a few minutes.
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It is the base version for the development of new features and methodology.
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All available simulation models are implemented in this version, including:
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1. **Point charge electrostatics + Lennard-Jones:** Original fully classical implementation allows the user to set up calculation without any ab initio input by specifying atomic positions, types and (optionally) charges.
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1. **Hartree-potential electrostatics + Lennard-Jones:** Electrostatics is considerably improved by using Hartree potential from DFT calculation and using the Quadrupole model for CO-tip.
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We found this crucial to properly simulate polar molecules (e.g. H2O clusters, carboxylic acids, PTCDA) which exhibit strong electrostatic distortions of AFM images.
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1. **Hartree-potential electrostatics + Density overlap:** Further accuracy improvement is achieved when Pauli repulsion between electron shells of atoms is modelled by the overlap between electron density of tip and sample.
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This repulsive term replaces the repulsive part of Lennard-Jones while the attractive part (C6) remains.
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This modification considerably improves especially the simulation of molecules with electron pairs (-NH-, -OH, =O group), triple bonds and other strongly concentrated electrons.
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1. **GPU version:** - Version specially designed for the generation of training data for machine learning.
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Implementation using `pyOpenCL` can parallelize the evaluation of forcefield and relaxation of probe-particle positions over hundreds or thousands of stream processors of the graphical accelerator.
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The further speed-up is achieved by using hardware-accelerated trilinear interpolation of 3D textures available in most GPUs.
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This allows simulating 10-100 AFM experiments per second on consumer-grade desktop GPU.
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_GPU version is designed to work in collaboration with machine-learning software for AFM (https://github.com/SINGROUP/ASD-AFM) and use various generators of molecular geometry._
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1. **GUI @ GPU** - The speed of GPU implementation enables interactive GUI where AFM images of molecules can be updated on the fly (<<0.1s) on a common laptop computer, while the user is editing molecular geometry or parameters of the tip.
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This provides an invaluable tool, especially for experimentalists trying to identify and interpret the structure and configuration of molecules in experiments on the fly while running the experiment.
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## Other branches
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* **master_backup** is the old `master` branch that was recently significantly updated and named `main`.
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For users who miss the old master branch, we provided a backup copy.
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However, this version is very old and its use is discouraged.
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* **PhotonMap** implements the latest developments concerning sub-molecular scanning probes combined with Raman spectroscopy (TERS) and fluorescent spectroscopy (LSTM).
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* **complex_tip** is a modification of the Probe-Particle Model with 2 particles that allows a better fit to experimental results at the cost of additional fitting parameters.
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## For contributors
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If you miss some functionality or have discovered issues with the latest release - let us know by creating [an issue](https://github.com/Probe-Particle/ppafm/issues/new).
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If you would like to contribute to the development of the ppafm code, please read the [Developer's Guide](https://github.com/Probe-Particle/ppafm/wiki/For-Developers) wiki page.
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Small improvements in the documentation or minor bug fixes are always welcome.
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## Further information
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- Wiki: https://github.com/Probe-Particle/ProbeParticleModel/wiki
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- Python API documentation: https://ppafm.readthedocs.io/en/latest/
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## Publications describing the Probe-Particle Model
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If you have used `ppafm` in your research, please cite the following articles:
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## License
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MIT
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