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arXiv · 2010.01358

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

Nobuyuki Yoshioka·Wataru Mizukami·Franco Nori
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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.

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BibTeXRIS

Nobuyuki Yoshioka, Wataru Mizukami, Franco Nori. 2020-10-03. Solving Quasiparticle Band Spectra of Real Solids using Neural-Network Quantum States. https://doi.org/10.1038/s42005-021-00609-0

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