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TorchMD: a deep learning framework for molecular simulations

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dc.contributor.author Doerr, Stefan, 1987-
dc.contributor.author Majewski, Maciej
dc.contributor.author Pérez, Adrià
dc.contributor.author Krämer, Andreas
dc.contributor.author Clementi, Cecilia
dc.contributor.author Noé, Frank
dc.contributor.author Giorgino, Toni
dc.contributor.author De Fabritiis, Gianni
dc.date.accessioned 2022-01-25T07:29:40Z
dc.date.available 2022-01-25T07:29:40Z
dc.date.issued 2021
dc.identifier.citation Doerr S, Majewski M, Pérez A, Krämer A, Clementi C, Noe F, Giorgino T, De Fabritiis G. TorchMD: a deep learning framework for molecular simulations. J Chem Theory Comput. 2021;17(4):2355-63. DOI: 10.1021/acs.jctc.0c01343
dc.identifier.issn 1549-9618
dc.identifier.uri http://hdl.handle.net/10230/52303
dc.description.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.
dc.description.sponsorship This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 823712 (CompBioMed2 Project).
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher American Chemical Society (ACS)
dc.relation.ispartof J Chem Theory Comput. 2021;17(4):2355-63
dc.rights © 2021 American Chemical Society. This work is licensed under a Creative Commons Attribution 4.0 International License
dc.rights.uri https://creativecommons.org/licenses/by/4.0/
dc.title TorchMD: a deep learning framework for molecular simulations
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.1021/acs.jctc.0c01343
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/823712
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion


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