Machine learning coarse grained models for water

H Chan and MJ Cherukara and B Narayanan and TD Loeffler and C Benmore and SK Gray and SKRS Sankaranarayanan, NATURE COMMUNICATIONS, 10, 379 (2019).

DOI: 10.1038/s41467-018-08222-6

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).

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