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Title: A coarse-grained deep neural network model for liquid water

Abstract

We introduce a coarse-grained deep neural network (CG-DNN) model for liquid water that utilizes 50 rotational and translational invariant coordinates and is trained exclusively against energies of ∼30 000 bulk water configurations. Our CG-DNN potential accurately predicts both the energies and the molecular forces of water, within 0.9 meV/molecule and 54 meV/Å of a reference (coarse-grained bond-order potential) model. The CG-DNN water model also provides good prediction of several structural, thermodynamic, and temperature dependent properties of liquid water, with values close to those obtained from the reference model. More importantly, CG-DNN captures the well-known density anomaly of liquid water observed in experiments. Our work lays the groundwork for a scheme where existing empirical water models can be utilized to develop a fully flexible neural network framework that can subsequently be trained against sparse data from high-fidelity albeit expensive beyond-DFT calculations.

Authors:
ORCiD logo [1];  [1];  [1]; ORCiD logo [1];  [2];  [3]
+ Show Author Affiliations
  1. Argonne National Lab. (ANL), Argonne, IL (United States). Center for Nanoscale Materials
  2. Univ. of Louisville, KY (United States)
  3. Argonne National Lab. (ANL), Argonne, IL (United States). Center for Nanoscale Materials; Univ. of Illinois, Chicago, IL (United States)
Publication Date:
Research Org.:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC); Univ. of California, Oakland, CA (United States); Argonne National Laboratory (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1577785
Alternate Identifier(s):
OSTI ID: 1573079
Grant/Contract Number:  
AC02-05CH11231; AC02-06CH11357; LDRD-2017-012-N0
Resource Type:
Accepted Manuscript
Journal Name:
Applied Physics Letters
Additional Journal Information:
Journal Volume: 115; Journal Issue: 19; Journal ID: ISSN 0003-6951
Publisher:
American Institute of Physics (AIP)
Country of Publication:
United States
Language:
English
Subject:
42 ENGINEERING; Physics

Citation Formats

Patra, Tarak K., Loeffler, Troy D., Chan, Henry, Cherukara, Mathew J., Narayanan, Badri, and Sankaranarayanan, Subramanian K. R. S. A coarse-grained deep neural network model for liquid water. United States: N. p., 2019. Web. doi:10.1063/1.5116591.
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Patra, Tarak K., Loeffler, Troy D., Chan, Henry, Cherukara, Mathew J., Narayanan, Badri, & Sankaranarayanan, Subramanian K. R. S. A coarse-grained deep neural network model for liquid water. United States. https://doi.org/10.1063/1.5116591
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Patra, Tarak K., Loeffler, Troy D., Chan, Henry, Cherukara, Mathew J., Narayanan, Badri, and Sankaranarayanan, Subramanian K. R. S. Mon . "A coarse-grained deep neural network model for liquid water". United States. https://doi.org/10.1063/1.5116591. https://www.osti.gov/servlets/purl/1577785.
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@article{osti_1577785,
title = {A coarse-grained deep neural network model for liquid water},
author = {Patra, Tarak K. and Loeffler, Troy D. and Chan, Henry and Cherukara, Mathew J. and Narayanan, Badri and Sankaranarayanan, Subramanian K. R. S.},
abstractNote = {We introduce a coarse-grained deep neural network (CG-DNN) model for liquid water that utilizes 50 rotational and translational invariant coordinates and is trained exclusively against energies of ∼30 000 bulk water configurations. Our CG-DNN potential accurately predicts both the energies and the molecular forces of water, within 0.9 meV/molecule and 54 meV/Å of a reference (coarse-grained bond-order potential) model. The CG-DNN water model also provides good prediction of several structural, thermodynamic, and temperature dependent properties of liquid water, with values close to those obtained from the reference model. More importantly, CG-DNN captures the well-known density anomaly of liquid water observed in experiments. Our work lays the groundwork for a scheme where existing empirical water models can be utilized to develop a fully flexible neural network framework that can subsequently be trained against sparse data from high-fidelity albeit expensive beyond-DFT calculations.},
doi = {10.1063/1.5116591},
journal = {Applied Physics Letters},
number = 19,
volume = 115,
place = {United States},
year = {Mon Nov 04 00:00:00 EST 2019},
month = {Mon Nov 04 00:00:00 EST 2019}
}
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Publisher's Version of Record
https://doi.org/10.1063/1.5116591
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