torch-pme
=========
.. image:: https://raw.githubusercontent.com/lab-cosmo/torch-pme/refs/heads/main/docs/src/logo/torch-pme.svg
:width: 200 px
:align: left
|tests| |codecov| |docs|
.. marker-introduction
``torch-pme`` enables efficient, auto-differentiable computation of long-range
interactions in PyTorch. Auto-differentiation is supported for particle *positions*,
*charges*, and *cell* parameters, allowing not only the automatic computation of forces
but also enabling general applications in machine learning tasks. The library offers
classes for Particle-Particle Particle-Mesh Ewald (P3M), Particle Mesh Ewald (``PME``),
standard ``Ewald``, and non-periodic methods, with the flexibility to calculate
potentials beyond :math:`1/r` electrostatics, including arbitrary order :math:`1/r^p`
potentials.
Optimized for both CPU and GPU devices, ``torch-pme`` is fully `TorchScriptable`_,
allowing it to be converted into a format that runs independently of Python, such as in
C++, making it ideal for high-performance production environments.
.. _`TorchScriptable`: https://pytorch.org/docs/stable/jit.html
.. marker-documentation
For details, tutorials, and examples, please have a look at our `documentation`_.
.. _`documentation`: https://lab-cosmo.github.io/torch-pme
.. marker-installation
Installation
------------
You can install *torch-pme* using pip with
.. code-block:: bash
pip install torch-pme
and ``import torchpme`` to use it in your projects!
We also provide bindings to `metatensor <https://docs.metatensor.org>`_ which
can optionally be installed together and used as ``torchpme.metatensor`` via
.. code-block:: bash
pip install torch-pme[metatensor]
.. marker-issues
Having problems or ideas?
-------------------------
Having a problem with torch-pme? Please let us know by `submitting an issue
<https://github.com/lab-cosmo/torch-pme/issues>`_.
Submit new features or bug fixes through a `pull request
<https://github.com/lab-cosmo/torch-pme/pulls>`_.
.. marker-cite
Reference
---------
If you use *torch-pme* for your work, please read and cite our preprint available on
`arXiv`_.
.. code-block::
@article{loche_fast_2024,
title = {Fast and Flexible Range-Separated Models for Atomistic Machine Learning},
author = {Loche, Philip and {Huguenin-Dumittan}, Kevin K. and Honarmand, Melika and Xu, Qianjun and Rumiantsev, Egor and How, Wei Bin and Langer, Marcel F. and Ceriotti, Michele},
year = {2024},
month = dec,
number = {arXiv:2412.03281},
eprint = {2412.03281},
primaryclass = {physics},
publisher = {arXiv},
doi = {10.48550/arXiv.2412.03281},
urldate = {2024-12-05},
archiveprefix = {arXiv}
}
.. _`arXiv`: http://arxiv.org/abs/2412.03281
.. marker-contributing
Contributors
------------
Thanks goes to all people that make torch-pme possible:
.. image:: https://contrib.rocks/image?repo=lab-cosmo/torch-pme
:target: https://github.com/lab-cosmo/torch-pme/graphs/contributors
.. |tests| image:: https://github.com/lab-cosmo/torch-pme/workflows/Tests/badge.svg
:alt: Github Actions Tests Job Status
:target: https://github.com/lab-cosmo/torch-pme/actions?query=workflow%3ATests
.. |codecov| image:: https://codecov.io/gh/lab-cosmo/torch-pme/graph/badge.svg?token=srVKRy7r6m
:alt: Code coverage
:target: https://codecov.io/gh/lab-cosmo/torch-pme
.. |docs| image:: https://img.shields.io/badge/documentation-latest-sucess
:alt: Documentation
:target: `documentation`_
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