Accurate nuclear quantum statistics on machine-learned classical effective potentials

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dc.citation.articleNumber134102en_US
dc.citation.issueNumber13en_US
dc.citation.journalTitleThe Journal of Chemical Physicsen_US
dc.citation.volumeNumber161en_US
dc.contributor.authorZaporozhets, Irynaen_US
dc.contributor.authorMusil, Félixen_US
dc.contributor.authorKapil, Venkaten_US
dc.contributor.authorClementi, Ceciliaen_US
dc.contributor.orgCenter for Theoretical Biological Physicsen_US
dc.date.accessioned2024-10-29T14:11:22Zen_US
dc.date.available2024-10-29T14:11:22Zen_US
dc.date.issued2024en_US
dc.description.abstractThe contribution of nuclear quantum effects (NQEs) to the properties of various hydrogen-bound systems, including biomolecules, is increasingly recognized. Despite the development of many acceleration techniques, the computational overhead of incorporating NQEs in complex systems is sizable, particularly at low temperatures. In this work, we leverage deep learning and multiscale coarse-graining techniques to mitigate the computational burden of path integral molecular dynamics (PIMD). In particular, we employ a machine-learned potential to accurately represent corrections to classical potentials, thereby significantly reducing the computational cost of simulating NQEs. We validate our approach using four distinct systems: Morse potential, Zundel cation, single water molecule, and bulk water. Our framework allows us to accurately compute position-dependent static properties, as demonstrated by the excellent agreement obtained between the machine-learned potential and computationally intensive PIMD calculations, even in the presence of strong NQEs. This approach opens the way to the development of transferable machine-learned potentials capable of accurately reproducing NQEs in a wide range of molecular systems.en_US
dc.identifier.citationZaporozhets, I., Musil, F., Kapil, V., & Clementi, C. (2024). Accurate nuclear quantum statistics on machine-learned classical effective potentials. The Journal of Chemical Physics, 161(13), 134102. https://doi.org/10.1063/5.0226764en_US
dc.identifier.digital134102_1_5-0226764en_US
dc.identifier.doihttps://doi.org/10.1063/5.0226764en_US
dc.identifier.urihttps://hdl.handle.net/1911/117954en_US
dc.language.isoengen_US
dc.publisherAIP Publishingen_US
dc.rightsExcept where otherwise noted, this work is licensed under a Creative Commons Attribution (CC BY) license. Permission to reuse, publish, or reproduce the work beyond the terms of the license or beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.titleAccurate nuclear quantum statistics on machine-learned classical effective potentialsen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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