Interatomic force from neural network based variational quantum Monte Carlo

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.

References in corpus (6)

• Stable liquid Hydrogen at high pressure by a novel ab-initio molecular dynamics
• Phase diagram of hydrogen and a hydrogen-helium mixture at planetary conditions by Quantum Monte Carlo simulations
• Unexpectedly high pressure for molecular dissociation in liquid hydrogen by a reliable electronic simulation
• Ab-initio molecular dynamics simulation of liquid water by Quantum Monte Carlo
• Heavy-tailed random error in quantum Monte Carlo
• Methods for calculating forces within quantum Monte Carlo simulations