Machine learning coarse grained models for water
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).
- Authors:
-
- 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
- Publication Date:
- Research Org.:
- Argonne National Laboratory (ANL), Argonne, IL (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES)
- OSTI Identifier:
- 1494795
- Grant/Contract Number:
- AC02-06CH11357; AC02-05CH11231
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Nature Communications
- Additional Journal Information:
- Journal Volume: 10; Journal ID: ISSN 2041-1723
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 42 ENGINEERING; coarse-grained models; molecular dynamics
Citation Formats
Chan, Henry, Cherukara, Mathew J., Narayanan, Badri, Loeffler, Troy D., Benmore, Chris, Gray, Stephen K., and Sankaranarayanan, Subramanian K. R. S. Machine learning coarse grained models for water. United States: N. p., 2019.
Web. doi:10.1038/s41467-018-08222-6.
Chan, Henry, Cherukara, Mathew J., Narayanan, Badri, Loeffler, Troy D., Benmore, Chris, Gray, Stephen K., & Sankaranarayanan, Subramanian K. R. S. Machine learning coarse grained models for water. United States. https://doi.org/10.1038/s41467-018-08222-6
Chan, Henry, Cherukara, Mathew J., Narayanan, Badri, Loeffler, Troy D., Benmore, Chris, Gray, Stephen K., and Sankaranarayanan, Subramanian K. R. S. Tue .
"Machine learning coarse grained models for water". United States. https://doi.org/10.1038/s41467-018-08222-6. https://www.osti.gov/servlets/purl/1494795.
@article{osti_1494795,
title = {Machine learning coarse grained models for water},
author = {Chan, Henry and Cherukara, Mathew J. and Narayanan, Badri and Loeffler, Troy D. and Benmore, Chris and Gray, Stephen K. and Sankaranarayanan, Subramanian K. R. S.},
abstractNote = {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).},
doi = {10.1038/s41467-018-08222-6},
journal = {Nature Communications},
number = ,
volume = 10,
place = {United States},
year = {Tue Jan 22 00:00:00 EST 2019},
month = {Tue Jan 22 00:00:00 EST 2019}
}
Web of Science
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