Machine learning coarse grained models for water

Henry Chan (), Mathew J. Cherukara, Badri Narayanan, Troy D. Loeffler, Chris Benmore, Stephen K. Gray and Subramanian K. R. S. Sankaranarayanan ()
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Henry Chan: Argonne National Laboratory
Mathew J. Cherukara: Argonne National Laboratory
Badri Narayanan: Argonne National Laboratory
Troy D. Loeffler: Argonne National Laboratory
Chris Benmore: Argonne National Laboratory
Stephen K. Gray: Argonne National Laboratory
Subramanian K. R. S. Sankaranarayanan: Argonne National Laboratory

Nature Communications, 2019, vol. 10, issue 1, 1-14

Abstract: Abstract 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-BOPdih, 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).

Date: 2019
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DOI: 10.1038/s41467-018-08222-6

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