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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.\n\nTorchMD 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.\n\nTorchMD is currently WIP so feel free to provide feedback on the API or potential bugs in the GitHub issue tracker.\n\nAlso check TorchMD-Net for fast and accurate neural network potentials https://github.com/torchmd/torchmd-net/\n\n## Citation\n\nPlease cite:\n\n```\n@misc{doerr2020torchmd,\n      title={TorchMD: A deep learning framework for molecular simulations},\n      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},\n      year={2020},\n      eprint={2012.12106},\n      archivePrefix={arXiv},\n      primaryClass={physics.chem-ph}\n}\n```\n\nTo reproduce the paper go to the tutorial notebook https://github.com/torchmd/torchmd-cg/blob/master/tutorial/Chignolin_Coarse-Grained_Tutorial.ipynb\n\n## License\n\nNote. 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\n\n## Installation\n\nWe recommend installing TorchMD in a new python environment ideally through the Miniforge package manager.\n\n```\nmamba create -n torchmd\nmamba activate torchmd\nmamba install pytorch python=3.10 -c conda-forge\nmamba install moleculekit parmed jupyter -c acellera -c conda-forge # For running the examples\npip install torchmd\n```\n\n## Examples\n\nVarious examples can be found in the `examples` folder on how to perform dynamics using TorchMD.\n\n## Help and comments\n\nPlease use the github issue of this repository.\n\n## Acknowledgements\n\nWe 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).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftorchmd%2Ftorchmd","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftorchmd%2Ftorchmd","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftorchmd%2Ftorchmd/lists"}