Coarse graining molecular dynamics with graph neural networks

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dc.citation.articleNumber194101
dc.citation.issueNumber19
dc.citation.journalTitleThe Journal of Chemical Physics
dc.citation.volumeNumber153
dc.contributor.authorHusic, Brooke E.
dc.contributor.authorCharron, Nicholas E.
dc.contributor.authorLemm, Dominik
dc.contributor.authorWang, Jiang
dc.contributor.authorPérez, Adrià
dc.contributor.authorMajewski, Maciej
dc.contributor.authorKrämer, Andreas
dc.contributor.authorChen, Yaoyi
dc.contributor.authorOlsson, Simon
dc.contributor.authorde Fabritiis, Gianni
dc.contributor.authorNoé, Frank
dc.contributor.authorClementi, Cecilia
dc.contributor.orgCenter for Theoretical Biological Physics
dc.date.accessioned2020-12-16T22:09:03Z
dc.date.available2020-12-16T22:09:03Z
dc.date.issued2020
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.
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.
dc.identifier.doihttps://doi.org/10.1063/5.0026133
dc.identifier.urihttps://hdl.handle.net/1911/109749
dc.language.isoeng
dc.publisherAmerican Institute of Physics
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.
dc.titleCoarse graining molecular dynamics with graph neural networks
dc.typeJournal article
dc.type.dcmiText
dc.type.publicationpublisher version
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