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

• URL: http://arxiv.org/abs/2010.01358v2
• Date: Mon, 24 May 2021 07:41:10 GMT
• Title: Solving Quasiparticle Band Spectra of Real Solids using Neural-Network Quantum States
• Authors: Nobuyuki Yoshioka and Wataru Mizukami and Franco Nori
• Abstract summary: We show that artificial neural networks 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. This work opens up a path to elucidate the intriguing and complex many-body phenomena in solid-state systems.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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