Interatomic force from neural network based variational quantum Monte Carlo

TL;DR

This paper explores the use of neural network wavefunctions in variational quantum Monte Carlo to improve the calculation of interatomic forces, which are crucial for simulating molecular and material structures.

Contribution

It introduces and tests neural network-based force estimators in VMC, demonstrating improved accuracy over traditional methods and providing insights into force calculation components.

Findings

Neural network ansatz enhances interatomic force calculations.

Force error correlates with neural network quality.

Different force terms contribute variably to computational cost.

Abstract

Accurate ab initio calculations are of fundamental importance in physics, chemistry, biology, and materials science, which have witnessed rapid development in the last couple of years with the help of machine learning computational techniques such as neural networks. Most of the recent efforts applying neural networks to ab initio calculation have been focusing on the energy of the system. In this study, we take a step forward and look at the interatomic force obtained with neural network wavefunction methods by implementing and testing several commonly used force estimators in variational quantum Monte Carlo (VMC). Our results show that neural network ansatz can improve the calculation of interatomic force upon traditional VMC. The relation between the force error and the quality of neural network, the contribution of different force terms, and the computational cost of each term are also discussed to provide guidelines for future applications. Our work demonstrates that it is promising to apply neural network wavefunction methods in simulating structures/dynamics of molecules/materials and provide training data for developing accurate force fields.