Coarse graining molecular dynamics with graph neural networks

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dc.citation.articleNumber194101en_US
dc.citation.issueNumber19en_US
dc.citation.journalTitleThe Journal of Chemical Physicsen_US
dc.citation.volumeNumber153en_US
dc.contributor.authorHusic, Brooke E.en_US
dc.contributor.authorCharron, Nicholas E.en_US
dc.contributor.authorLemm, Dominiken_US
dc.contributor.authorWang, Jiangen_US
dc.contributor.authorPérez, Adriàen_US
dc.contributor.authorMajewski, Maciejen_US
dc.contributor.authorKrämer, Andreasen_US
dc.contributor.authorChen, Yaoyien_US
dc.contributor.authorOlsson, Simonen_US
dc.contributor.authorde Fabritiis, Giannien_US
dc.contributor.authorNoé, Franken_US
dc.contributor.authorClementi, Ceciliaen_US
dc.contributor.orgCenter for Theoretical Biological Physicsen_US
dc.date.accessioned2020-12-16T22:09:03Zen_US
dc.date.available2020-12-16T22:09:03Zen_US
dc.date.issued2020en_US
dc.description.abstractCoarse graining enables the investigation of molecular dynamics for larger systems and at longer timescales than is possible at an atomic resolution. However, a coarse graining model must be formulated such that the conclusions we draw from it are consistent with the conclusions we would draw from a model at a finer level of detail. It has been proved that a force matching scheme defines a thermodynamically consistent coarse-grained model for an atomistic system in the variational limit. Wang et al. [ACS Cent. Sci. 5, 755 (2019)] demonstrated that the existence of such a variational limit enables the use of a supervised machine learning framework to generate a coarse-grained force field, which can then be used for simulation in the coarse-grained space. Their framework, however, requires the manual input of molecular features to machine learn the force field. In the present contribution, we build upon the advance of Wang et al. and introduce a hybrid architecture for the machine learning of coarse-grained force fields that learn their own features via a subnetwork that leverages continuous filter convolutions on a graph neural network architecture. We demonstrate that this framework succeeds at reproducing the thermodynamics for small biomolecular systems. Since the learned molecular representations are inherently transferable, the architecture presented here sets the stage for the development of machine-learned, coarse-grained force fields that are transferable across molecular systems.en_US
dc.identifier.citationHusic, Brooke E., Charron, Nicholas E., Lemm, Dominik, et al.. "Coarse graining molecular dynamics with graph neural networks." <i>The Journal of Chemical Physics,</i> 153, no. 19 (2020) American Institute of Physics: https://doi.org/10.1063/5.0026133.en_US
dc.identifier.doihttps://doi.org/10.1063/5.0026133en_US
dc.identifier.urihttps://hdl.handle.net/1911/109749en_US
dc.language.isoengen_US
dc.publisherAmerican Institute of Physicsen_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.titleCoarse graining molecular dynamics with graph neural networksen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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