https://github.com/torchmd/torchmd

End-To-End Molecular Dynamics (MD) Engine using PyTorch
https://github.com/torchmd/torchmd

coarse-grained-molecular-dynamics machine-learning molecular-dynamics neural-networks pytorch pytorch-lightning

Last synced: 28 days ago
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End-To-End Molecular Dynamics (MD) Engine using PyTorch

• Host: GitHub
• URL: https://github.com/torchmd/torchmd
• Owner: torchmd
• License: mit
• Created: 2020-02-12T14:00:42.000Z (almost 5 years ago)
• Default Branch: main
• Last Pushed: 2024-02-22T08:42:48.000Z (10 months ago)
• Last Synced: 2024-05-16T05:08:47.406Z (7 months ago)
• Topics: coarse-grained-molecular-dynamics, machine-learning, molecular-dynamics, neural-networks, pytorch, pytorch-lightning
• Language: Python
• Homepage:
• Size: 2.5 MB
• Stars: 512
• Watchers: 17
• Forks: 73
• Open Issues: 7
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

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README

        
# TorchMD

## About

TorchMD intends to provide a simple to use API for performing molecular dynamics using PyTorch. This enables researchers to more rapidly do research in force-field development as well as integrate seamlessly neural network potentials into the dynamics, with the simplicity and power of PyTorch.

TorchMD uses chemical units consistent with classical MD codes such as ACEMD, namely kcal/mol for energies, K for temperatures, g/mol for masses, and Å for distances.

TorchMD is currently WIP so feel free to provide feedback on the API or potential bugs in the GitHub issue tracker.

Also check TorchMD-Net for fast and accurate neural network potentials https://github.com/torchmd/torchmd-net/

## Citation

Please cite:

```

@misc{doerr2020torchmd,

      title={TorchMD: A deep learning framework for molecular simulations},

      author={Stefan Doerr and Maciej Majewsk and Adrià Pérez and Andreas Krämer and Cecilia Clementi and Frank Noe and Toni Giorgino and Gianni De Fabritiis},

      year={2020},

      eprint={2012.12106},

      archivePrefix={arXiv},

      primaryClass={physics.chem-ph}

}

```

To reproduce the paper go to the tutorial notebook https://github.com/torchmd/torchmd-cg/blob/master/tutorial/Chignolin_Coarse-Grained_Tutorial.ipynb

## License

Note. All the code in this repository is MIT, however we use several file format readers that are taken from Moleculekit which has a free open source non-for-profit, research license. This is mainly in torchmd/run.py. Moleculekit is installed automatically being in the requirement file. Check out Moleculekit here: https://github.com/Acellera/moleculekit

## Installation

We recommend installing TorchMD in a new python environment ideally through the Miniforge package manager.

```

mamba create -n torchmd

mamba activate torchmd

mamba install pytorch python=3.10 -c conda-forge

mamba install moleculekit parmed jupyter -c acellera -c conda-forge # For running the examples

pip install torchmd

```

## Examples

Various examples can be found in the `examples` folder on how to perform dynamics using TorchMD.

## Help and comments

Please use the github issue of this repository.

## Acknowledgements

We would like to acknowledge funding by the Chan Zuckerberg Initiative and Acellera in support of this project. This project will be now developed in collaboration with openMM (www.openmm.org) and acemd (www.acellera.com/acemd).