Machine learning coarse grained models for water

Chan, Henry; Cherukara, Mathew J.; Narayanan, Badri; Loeffler, Troy D.; Benmore, Chris; Gray, Stephen K.; Sankaranarayanan, Subramanian K. R. S.

doi:10.1038/s41467-018-08222-6

Title: Machine learning coarse grained models for water

Journal Article · Tue Jan 22 00:00:00 EST 2019 · Nature Communications

DOI:https://doi.org/10.1038/s41467-018-08222-6· OSTI ID:1494795

^[1];

^[1]; Narayanan, Badri ^[2]; Loeffler, Troy D. ^[1];

^[3]; Gray, Stephen K. ^[4]; Sankaranarayanan, Subramanian K. R. S. ^[4]

Argonne National Lab. (ANL), Argonne, IL (United States). Center for Nanoscale Materials
Argonne National Lab. (ANL), Argonne, IL (United States). Center for Nanoscale Materials; Univ. of Louisville, KY (United States). Dept. of Mechanical Engineering
Argonne National Lab. (ANL), Argonne, IL (United States). X-ray Science Division
Argonne National Lab. (ANL), Argonne, IL (United States). Center for Nanoscale Materials; Univ. of Chicago, IL (United States). Consortium for Advanced Science and Engineering

An accurate and computationally efficient molecular level description of mesoscopic behavior of ice-water systems remains a major challenge. Here, we introduce a set of machine-learned coarse-grained (CG) models (ML-BOP, ML-BOP_dih, and ML-mW) that accurately describe the structure and thermodynamic anomalies of both water and ice at mesoscopic scales, all at two orders of magnitude cheaper computational cost than existing atomistic models. In a significant departure from conventional force-field fitting, we use a multilevel evolutionary strategy that trains CG models against not just energetics from first-principles and experiments but also temperature-dependent properties inferred from on-the-fly molecular dynamics (~ 10’s of milliseconds of overall trajectories). Our ML BOP models predict both the correct experimental melting point of ice and the temperature of maximum density of liquid water that remained elusive to-date. Our ML workflow navigates efficiently through the high-dimensional parameter space to even improve upon existing high-quality CG models (e.g. mW model).

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Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: AC02-06CH11357; AC02-05CH11231

OSTI ID:: 1494795

Journal Information:: Nature Communications, Vol. 10; ISSN 2041-1723

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 87 works

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