Cited by 230Machine Learning in Materials ScienceAdvanced Physical and Chemical Molecular InteractionsAdvanced Chemical Physics Studies

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TorchMD: A Deep Learning Framework for Molecular Simulations

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Doerr, Stefan, Majewski, Maciej, Pérez, Adrià, Krämer, Andreas, Clementi, Cecilia, Noe, Frank, Giorgino, Toni, De Fabritiis, Gianni·2021·Journal of Chemical Theory and Computation

DOI 10.1021/acs.jctc.0c01343PMC PMC8486166

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Abstract

Molecular dynamics simulations provide a mechanistic description of molecules by relying on empirical potentials. The quality and transferability of such potentials can be improved leveraging data-driven models derived with machine learning approaches. Here, we present TorchMD, a framework for molecular simulations with mixed classical and machine learning potentials. All force computations including bond, angle, dihedral, Lennard-Jones, and Coulomb interactions are expressed as PyTorch arrays and operations. Moreover, TorchMD enables learning and simulating neural network potentials. We validate it using standard Amber all-atom simulations, learning an ab initio potential, performing an end-to-end training, and finally learning and simulating a coarse-grained model for protein folding. We believe that TorchMD provides a useful tool set to support molecular simulations of machine learning potentials. Code and data are freely available at github.com/torchmd.

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Authors: Doerr, S., Majewski, M., Pérez, A., Krämer, A., Clementi, C., Noe, F., Giorgino, T., & De Fabritiis, G.
Affiliations: Acellera (Spain) (ES)
Universitat Pompeu Fabra (ES)
Freie Universität Berlin (DE)
Rice University (US)
Department of Mathematics and Computer Science, Freie Universität, 14195 Berlin, Germany
Biophysics Institute, National Research Council (CNR-IBF), 20133 Milano, Italy
Acellera, 08005 Barcelona, Spain
ORCID: Doerr, Stefan
Majewski, Maciej
Pérez, Adrià
Krämer, Andreas
Clementi, Cecilia
Giorgino, Toni
De Fabritiis, Gianni
Venue: Journal of Chemical Theory and Computation
Publisher: American Chemical Society (ACS)
Published: 2021-03-17
Volume: 17
Issue: 4
Pages: 2355-2363
ISSN: 1549-9618 (ISSN-L)
1549-9626
DOI: 10.1021/acs.jctc.0c01343
PMC: PMC8486166
OpenAlex: W3113616921
Cited by: 230
References: 60
Citations by year: 2026: 30 · 2025: 51 · 2024: 52 · 2023: 61 · 2022: 24 · 2021: 12
FWCI: 11.31
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (unspecified)
Locations: Journal of Chemical Theory and Computation (publishedVersion, cc-by)
Europe PMC (PubMed Central) (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
arXiv (Cornell University) (submittedVersion)
Repositori digital de la UPF (Universitat Pompeu Fabra) (submittedVersion, cc-by)
arXiv (Cornell University)
Funders: Chan Zuckerberg Initiative
Horizon 2020 Framework Programme (823712)
Topics: Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
Physical Sciences › Chemistry › Physical and Theoretical Chemistry › Advanced Physical and Chemical Molecular Interactions
Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
Keywords: molecular dynamics, machine learning interatomic potentials, neural network potentials, Lennard-Jones interactions, electrostatic interactions, ab initio potentials, protein folding, coarse-grained models, PyTorch

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TorchMD: A Deep Learning Framework for Molecular Simulations

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