Solving quasiparticle band spectra of real solids using neural-network quantum states

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. Designing computational methods that can accurately predict useful material properties is an attractive alternative to cumbersome trial and error experimental approaches. Here, the authors present a computational method based on neural-network quantum states, which can reveal many-body quantum phenomena of a solid state system similar to first-principles calculations.

Automated summary

This study demonstrates the potential of artificial neural networks in predicting properties of solid systems, accurately simulating ground-state energies and quasiparticle band spectra, providing a new computational method for elucidating complex many-body phenomena in solid-state systems.