Details

Autor(en) / Beteiligte

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

Titel

Machine learning coarse grained models for water

Ist Teil von

• Nature communications, 2019-01, Vol.10 (1), p.379-379, Article 379

Ort / Verlag

England: Nature Publishing Group

Erscheinungsjahr

2019

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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 , 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).