Machine Learning of Coarse-Grained Molecular Dynamics Force Fields

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dc.citation.firstpage755en_US
dc.citation.issueNumber5en_US
dc.citation.journalTitleACS Central Scienceen_US
dc.citation.lastpage767en_US
dc.citation.volumeNumber5en_US
dc.contributor.authorWang, Jiangen_US
dc.contributor.authorOlsson, Simonen_US
dc.contributor.authorWehmeyer, Christophen_US
dc.contributor.authorPérez, Adriàen_US
dc.contributor.authorCharron, Nicholas E.en_US
dc.contributor.authorde Fabritiis, Giannien_US
dc.contributor.authorNoé, Franken_US
dc.contributor.authorClementi, Ceciliaen_US
dc.date.accessioned2019-12-05T18:52:38Zen_US
dc.date.available2019-12-05T18:52:38Zen_US
dc.date.issued2019en_US
dc.description.abstractAtomistic or ab initio molecular dynamics simulations are widely used to predict thermodynamics and kinetics and relate them to molecular structure. A common approach to go beyond the time- and length-scales accessible with such computationally expensive simulations is the definition of coarse-grained molecular models. Existing coarse-graining approaches define an effective interaction potential to match defined properties of high-resolution models or experimental data. In this paper, we reformulate coarse-graining as a supervised machine learning problem. We use statistical learning theory to decompose the coarse-graining error and cross-validation to select and compare the performance of different models. We introduce CGnets, a deep learning approach, that learns coarse-grained free energy functions and can be trained by a force-matching scheme. CGnets maintain all physically relevant invariances and allow one to incorporate prior physics knowledge to avoid sampling of unphysical structures. We show that CGnets can capture all-atom explicit-solvent free energy surfaces with models using only a few coarse-grained beads and no solvent, while classical coarse-graining methods fail to capture crucial features of the free energy surface. Thus, CGnets are able to capture multibody terms that emerge from the dimensionality reduction.en_US
dc.identifier.citationWang, Jiang, Olsson, Simon, Wehmeyer, Christoph, et al.. "Machine Learning of Coarse-Grained Molecular Dynamics Force Fields." <i>ACS Central Science,</i> 5, no. 5 (2019) American Chemical Society: 755-767. https://doi.org/10.1021/acscentsci.8b00913.en_US
dc.identifier.digitalacscentsci.8b00913en_US
dc.identifier.doihttps://doi.org/10.1021/acscentsci.8b00913en_US
dc.identifier.urihttps://hdl.handle.net/1911/107778en_US
dc.language.isoengen_US
dc.publisherAmerican Chemical Societyen_US
dc.rightsThis is an open access article published under an ACS AuthorChoice License, which permits copying and redistribution of the article or any adaptations for non-commercial purposes.en_US
dc.rights.urihttps://pubs.acs.org/page/policy/authorchoice_termsofuse.htmlen_US
dc.titleMachine Learning of Coarse-Grained Molecular Dynamics Force Fieldsen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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