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 (18)

• Deep neural network solution of the electronic Schrödinger equation
• Ab-Initio Solution of the Many-Electron Schrödinger Equation with Deep Neural Networks
• Monte Carlo errors with less errors
• Fermionic neural-network states for ab-initio electronic structure
• Solving Many-Electron Schrödinger Equation Using Deep Neural Networks
• QMCPACK: Advances in the development, efficiency, and application of auxiliary field and real-space variational and diffusion Quantum Monte Carlo
• Variational and Diffusion Quantum Monte Carlo Calculations with the CASINO Code
• Stable liquid Hydrogen at high pressure by a novel ab-initio molecular dynamics
• Zero-Variance Zero-Bias Principle for Observables in quantum Monte Carlo: Application to Forces
• Correlated sampling in quantum Monte Carlo: a route to forces
• Algorithmic differentiation and the calculation of forces by quantum Monte Carlo
• 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
• TurboRVB: a many-body toolkit for {\it ab initio} electronic simulations by quantum Monte Carlo
• Heavy-tailed random error in quantum Monte Carlo
• Methods for calculating forces within quantum Monte Carlo simulations
• Gold-standard solutions to the Schrödinger equation using deep learning: How much physics do we need?

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• Electric Polarization from Many-Body Neural Network Ansatz
• Deep learning quantum Monte Carlo for solids
• Deep quantum Monte Carlo approach for polaritonic chemistry
• Fast Evaluation of Unbiased Atomic Forces in ab initio Variational Monte Carlo via the Lagrangian Technique