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 discretized 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 generalized 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.

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

“…As a remedy, several studies have been performed by training an MLP using higher rungs of KS-DFT (e.g., hybrid-DFT or meta-GGA) and then using this potential in PIMD simulations. 575 , 579 − 581 The study of water mentioned in the previous section, which used MLPs trained from hybrid DFT, revealed that NQEs were critical for promoting the hexagonal packing of molecules inside ice that ultimately lead to the 6-fold symmetry of snowflakes. 575 Highly data efficient ML potentials can even be trained on reference data at the computationally very expensive quantum-chemical CCSD(T) level of accuracy.…”

Enabling Large-Scale Condensed-Phase Hybrid Density Functional Theory Based Ab Initio Molecular Dynamics. 1. Theory, Algorithm, and Performance

“…that can be used to learn complex highdimensional PES via machine learning (ML) based approaches. [17][18][19][20] Assuming that the system is ergodic, the accuracy of a given MD simulation in predicting equilibrium properties is primarily governed by the quality of the ionic forces and stress tensor (or cell forces) used when propagating the corresponding equations of motion. As such, a physically sound approach for obtaining these forces is given by first-principles based electronic structure theories, which are the foundation for ab initio MD (AIMD) simulations.…”

Free energy of proton transfer at the water–TiO2 interface from ab initio deep potential molecular dynamics

“…21 In this work we extend both the time and length scales sampled by AIMD using a deep neural network (DNN) to represent the potential energy surface (PES), gaining a substantial speed-up of MD simulations. Several authors have demonstrated the DNNs' ability to reproduce the complex PES of ab initio potentials, [32][33][34] thus allowing accurate long-time simulations of several condensed phase systems [35][36][37] including metal oxide-water interfaces. 38,39 In the present study, the ab initio-based DNN potential reproduces energy and atomic forces obtained from DFT at a ve orders of magnitude lower computational cost.…”