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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 |