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.
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28 July 2020
Research Article|
July 27 2020
Temperature dependence of nuclear quantum effects on liquid water via artificial neural network model based on SCAN meta-GGA functional
Special Collection:
Machine Learning Meets Chemical Physics
Yi Yao
;
Yi Yao
a)
1
Department of Chemistry, University of North Carolina at Chapel Hill
, Chapel Hill, North Carolina 27599, USA
2
Department of Mechanical Engineering and Materials Science, Duke University
, Durham, North Carolina 27708, USA
a)Author to whom correspondence should be addressed: [email protected]
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Yosuke Kanai
Yosuke Kanai
a)
1
Department of Chemistry, University of North Carolina at Chapel Hill
, Chapel Hill, North Carolina 27599, USA
a)Author to whom correspondence should be addressed: [email protected]
Search for other works by this author on:
a)Author to whom correspondence should be addressed: [email protected]
Note: This paper is part of the JCP Special Topic on Machine Learning Meets Chemical Physics.
J. Chem. Phys. 153, 044114 (2020)
Article history
Received:
May 06 2020
Accepted:
July 06 2020
Citation
Yi Yao, Yosuke Kanai; Temperature dependence of nuclear quantum effects on liquid water via artificial neural network model based on SCAN meta-GGA functional. J. Chem. Phys. 28 July 2020; 153 (4): 044114. https://doi.org/10.1063/5.0012815
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