Welcome to the TorchMD-NET Documentation!

TorchMD-NET provides state-of-the-art neural networks potentials (NNPs) and a mechanism to train them. It offers efficient and fast implementations of several NNPs and is integrated with GPU-accelerated molecular dynamics code like ACEMD, OpenMM, and TorchMD. TorchMD-NET exposes its NNPs as PyTorch modules.

Cite

If you use TorchMD-NET in your research, please cite the following papers:

Main reference

@misc{
pelaez2024torchmdnet,
title={TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations},
author={Raul P. Pelaez and Guillem Simeon and Raimondas Galvelis and Antonio Mirarchi and Peter Eastman and Stefan Doerr and Philipp Thölke and Thomas E. Markland and Gianni De Fabritiis},
year={2024},
eprint={2402.17660},
archivePrefix={arXiv},
primaryClass={cs.LG}
}

TensorNet

@inproceedings{
simeon2023tensornet,
title={TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentials},
author={Guillem Simeon and Gianni De Fabritiis},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023},
url={https://openreview.net/forum?id=BEHlPdBZ2e}
}

Equivariant Transformer

@inproceedings{
tholke2021equivariant,
title={Equivariant Transformers for Neural Network based Molecular Potentials},
author={Philipp Th{\"o}lke and Gianni De Fabritiis},
booktitle={International Conference on Learning Representations},
year={2022},
url={https://openreview.net/forum?id=zNHzqZ9wrRB}
}

Graph Network

@article{Majewski2023,
title = {Machine learning coarse-grained potentials of protein thermodynamics},
volume = {14},
ISSN = {2041-1723},
url = {http://dx.doi.org/10.1038/s41467-023-41343-1},
DOI = {10.1038/s41467-023-41343-1},
number = {1},
journal = {Nature Communications},
publisher = {Springer Science and Business Media LLC},
author = {Majewski,  Maciej and Pérez,  Adrià and Th\"{o}lke,  Philipp and Doerr,  Stefan and Charron,  Nicholas E. and Giorgino,  Toni and Husic,  Brooke E. and   Clementi,  Cecilia and Noé,  Frank and De Fabritiis,  Gianni},
year = {2023},
month = sep
}