Isotope effects in liquid water via deep potential molecular dynamics

Abstract

A comprehensive microscopic understanding of ambient liquid water is a major challenge for ab initio simulations as it simultaneously requires an accurate quantum mechanical description of the underlying potential energy surface (PES) as well as extensive sampling of configuration space. Due to the presence of light atoms (e.g. $H$ or $D$), nuclear quantum fluctuations lead to observable changes in the structural properties of liquid water (e.g. isotope effects), and therefore provide yet another challenge for ab initio approaches. In this work, we demonstrate that the combination of dispersion-inclusive hybrid density functional theory (DFT), the Feynman discretised path-integral (PI) approach, and machine learning (ML) constitutes a versatile ab initio based framework that enables extensive sampling of both thermal and nuclear quantum fluctuations on a quite accurate underlying PES. In particular, we employ the recently developed deep potential molecular dynamics (DPMD) model – a neural-network representation of the ab initio PES – in conjunction with a PI approach based on the generalised Langevin equation (PIGLET) to investigate how isotope effects influence the structural properties of ambient liquid $H2O$ and $D2O$. Through a detailed analysis of the interference differential cross sections as well as several radial and angular distribution functions, we demonstrate that this approach can furnish a semi-quantitative prediction of these subtle isotope effects.

GRAPHICAL ABSTRACT

Keywords:

• Liquid water
• nuclear quantum effects
• ab initio molecular dynamics
• deep neural network
• isotope effects

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Hsin-Yu Ko  http://orcid.org/0000-0003-1619-6514

Linfeng Zhang  http://orcid.org/0000-0002-8470-5846

Biswajit Santra  http://orcid.org/0000-0003-3609-2106

Han Wang  http://orcid.org/0000-0001-5623-1148

Weinan E  http://orcid.org/0000-0003-0272-9500

Robert A. DiStasio Jr  http://orcid.org/0000-0003-2732-194X

Roberto Car  http://orcid.org/0000-0001-5243-2647

Notes

1 Data source: https://www.ncnr.nist.gov/resources/n-lengths/elements/

Additional information

Funding

HYK, LZ, WE, and RC gratefully acknowledge support from the U.S. Department of Energy (DOE) under [Grant number DE-SC0019394]. HW gratefully acknowledges support from the National Natural Science Foundation of China (NSFC) under [Grant number 11871110], as well as the National Key Research and Development Program of China under [Grant numbers 2016YFB0201200 and 2016YFB0201203]. LZ and WE also acknowledge support from the Major Program of the National Natural Science Foundation of China (NNSFC) under [Grant numbers 91130005 and U1430237], as well as the Office of Naval Research (ONR) [Grant number N00014-13-1-0338]. RD acknowledges the support of the Center for Alkaline Based Energy Solutions (CABES), an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Award No. DE-SC0019445. This work used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.