Solving Quasiparticle Band Spectra of Real Solids using Neural-Network Quantum States

TL;DR

This paper introduces a neural network-based method for accurately simulating the quantum many-body wave functions of real solids, enabling precise calculation of ground-state energies and quasiparticle band spectra.

Contribution

It demonstrates that neural networks can efficiently encode complex wave functions of extended solids and extract quasiparticle spectra, advancing ab initio solid-state calculations.

Findings

Achieved chemical accuracy in ground-state energy simulations for 1D, 2D, and 3D solids.

Developed a computational technique to extract quasiparticle band spectra from neural network wave functions.

Showed neural networks' potential for elucidating many-body phenomena in solid-state physics.

Abstract

Establishing a predictive ab initio method for solid systems is one of the fundamental goals in condensed matter physics and computational materials science. The central challenge is how to encode a highly-complex quantum-many-body wave function compactly. Here, we demonstrate that artificial neural networks, known for their overwhelming expressibility in the context of machine learning, are excellent tool for first-principles calculations of extended periodic materials. We show that the ground-state energies in real solids in one-, two-, and three-dimensional systems are simulated precisely, reaching their chemical accuracy. The highlight of our work is that the quasiparticle band spectra, which are both essential and peculiar to solid-state systems, can be efficiently extracted with a computational technique designed to exploit the low-lying energy structure from neural networks. This work opens up a path to elucidate the intriguing and complex many-body phenomena in solid-state systems.