Deep learning quantum Monte Carlo for solids

TL;DR

Deep learning-enhanced quantum Monte Carlo methods have revolutionized ab initio calculations for solids, enabling accurate predictions of electronic properties and extending to complex periodic systems.

Contribution

This paper reviews the theoretical foundations, neural network wavefunction ansatz, and methodological advances of deep learning QMC for solids, highlighting recent applications and challenges.

Findings

Successfully computed energy and electron density of real solids.

Extended methods to periodic systems and finite temperature calculations.

Demonstrated potential of deep learning QMC in materials science.

Abstract

Deep learning has deeply changed the paradigms of many research fields. At the heart of chemical and physical sciences is the accurate ab initio calculation of many-body wavefunction, which has become one of the most notable examples to demonstrate the power of deep learning in science. In particular, the introduction of deep learning into quantum Monte Carlo (QMC) has significantly advanced the frontier of ab initio calculation, offering a universal tool to solve the electronic structure of materials and molecules. Deep learning QMC architectures were initial designed and tested on small molecules, focusing on comparisons with other state-of-the-art ab initio methods. Methodological developments, including extensions to real solids and periodic models, have been rapidly progressing and reported applications are fast expanding. This review covers the theoretical foundation of deep learning QMC for solids, the neural network wavefunction ansatz, and various of other methodological developments. Applications on computing energy, electron density, electric polarization, force and stress of real solids are also reviewed. The methods have also been extended to other periodic systems and finite temperature calculations. The review highlights the potentials and existing challenges of deep learning QMC in materials chemistry and condensed matter physics.