Toward High-level Machine Learning Potential for Water Based on Quantum Fragmentation and Neural Networks

Accurate
and efficient simulation of liquids, such as water and
salt solutions, using high-level wave function theories is still a
formidable task for computational chemists owing to the high computational
costs. In this study, we develop a deep machine learning potential
based on fragment-based second-order Møller–Plesset perturbation
theory (DP-MP2) for water through neural networks. We show that the
DP-MP2 potential predicts the structural, dynamical, and thermodynamic
properties of liquid water in better agreement with the experimental
data than previous studies based on density functional theory (DFT).
The nuclear quantum effects (NQEs) on the properties of liquid water
are also examined, which are noticeable in affecting the structural
and dynamical properties of liquid water under ambient conditions.
This work provides a general framework for quantitative predictions
of the properties of condensed-phase systems with the accuracy of
high-level wave function theory while achieving significant computational
savings compared to ab initio simulations.