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Title: Temperature dependence of nuclear quantum effects on liquid water via artificial neural network model based on SCAN meta-GGA functional

Abstract

We investigate the temperature dependence of nuclear quantum effects (NQEs) on structural and dynamic properties of liquid water by training a neural network force field using first-principles molecular dynamics (FPMD) based on the strongly constrained and appropriately normed meta-generalized gradient approximation exchange-correlation approximation. The FPMD simulation based on density functional theory has become a powerful computational approach for studying a wide range of condensed phase systems. However, its large computational cost makes it difficult to incorporate NQEs in the simulation and investigate temperature dependence of various properties. To circumvent this difficulty, we use an artificial neural network model and employ the thermostatted ring polymer MD approach for studying the temperature dependence of NQEs on various properties. The NQEs generally bring the radial distribution functions closer to the experimental measurements. Translational diffusivity and rotational dynamics of water molecules are both slowed down by the NQEs. The competing inter-molecular and intra-molecular quantum effects on hydrogen bonds, as discussed by Habershon, Markland, and Manolopoulos [J. Chem. Phys. 131(2), 024501 (2019)], can explain the observed temperature dependence of the NQEs on the dynamical properties in our simulation.

Authors:
ORCiD logo; ORCiD logo
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1643192
Grant/Contract Number:  
AC02-06CH11357
Resource Type:
Publisher's Accepted Manuscript
Journal Name:
Journal of Chemical Physics
Additional Journal Information:
Journal Name: Journal of Chemical Physics Journal Volume: 153 Journal Issue: 4; Journal ID: ISSN 0021-9606
Publisher:
American Institute of Physics
Country of Publication:
United States
Language:
English

Citation Formats

Yao, Yi, and Kanai, Yosuke. Temperature dependence of nuclear quantum effects on liquid water via artificial neural network model based on SCAN meta-GGA functional. United States: N. p., 2020. Web. doi:10.1063/5.0012815.
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Yao, Yi, & Kanai, Yosuke. Temperature dependence of nuclear quantum effects on liquid water via artificial neural network model based on SCAN meta-GGA functional. United States. https://doi.org/10.1063/5.0012815
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Yao, Yi, and Kanai, Yosuke. Mon . "Temperature dependence of nuclear quantum effects on liquid water via artificial neural network model based on SCAN meta-GGA functional". United States. https://doi.org/10.1063/5.0012815.
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@article{osti_1643192,
title = {Temperature dependence of nuclear quantum effects on liquid water via artificial neural network model based on SCAN meta-GGA functional},
author = {Yao, Yi and Kanai, Yosuke},
abstractNote = {We investigate the temperature dependence of nuclear quantum effects (NQEs) on structural and dynamic properties of liquid water by training a neural network force field using first-principles molecular dynamics (FPMD) based on the strongly constrained and appropriately normed meta-generalized gradient approximation exchange-correlation approximation. The FPMD simulation based on density functional theory has become a powerful computational approach for studying a wide range of condensed phase systems. However, its large computational cost makes it difficult to incorporate NQEs in the simulation and investigate temperature dependence of various properties. To circumvent this difficulty, we use an artificial neural network model and employ the thermostatted ring polymer MD approach for studying the temperature dependence of NQEs on various properties. The NQEs generally bring the radial distribution functions closer to the experimental measurements. Translational diffusivity and rotational dynamics of water molecules are both slowed down by the NQEs. The competing inter-molecular and intra-molecular quantum effects on hydrogen bonds, as discussed by Habershon, Markland, and Manolopoulos [J. Chem. Phys. 131(2), 024501 (2019)], can explain the observed temperature dependence of the NQEs on the dynamical properties in our simulation.},
doi = {10.1063/5.0012815},
journal = {Journal of Chemical Physics},
number = 4,
volume = 153,
place = {United States},
year = {Mon Jul 27 00:00:00 EDT 2020},
month = {Mon Jul 27 00:00:00 EDT 2020}
}
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Climbing the ladder of density functional approximations
journal, September 2013

Probing Defects and Correlations in the Hydrogen-Bond Network of ab Initio Water
journal, March 2016

  • Gasparotto, Piero; Hassanali, Ali A.; Ceriotti, Michele
  • Journal of Chemical Theory and Computation, Vol. 12, Issue 4
  • DOI: 10.1021/acs.jctc.5b01138

On the orientational relaxation of HDO in liquid water
journal, September 2005

  • Rezus, Y. L. A.; Bakker, H. J.
  • The Journal of Chemical Physics, Vol. 123, Issue 11
  • DOI: 10.1063/1.2009729

Equilibration and analysis of first-principles molecular dynamics simulations of water
journal, March 2018

  • Dawson, William; Gygi, François
  • The Journal of Chemical Physics, Vol. 148, Issue 12
  • DOI: 10.1063/1.5018116

Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density
journal, January 1988

Nuclear quantum effects and hydrogen bond fluctuations in water
journal, September 2013

  • Ceriotti, M.; Cuny, J.; Parrinello, M.
  • Proceedings of the National Academy of Sciences, Vol. 110, Issue 39
  • DOI: 10.1073/pnas.1308560110

Perspective: How good is DFT for water?
journal, April 2016

  • Gillan, Michael J.; Alfè, Dario; Michaelides, Angelos
  • The Journal of Chemical Physics, Vol. 144, Issue 13
  • DOI: 10.1063/1.4944633

Structure and dynamics of aqueous solutions from PBE-based first-principles molecular dynamics simulations
journal, October 2016

  • Pham, Tuan Anh; Ogitsu, Tadashi; Lau, Edmond Y.
  • The Journal of Chemical Physics, Vol. 145, Issue 15
  • DOI: 10.1063/1.4964865
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