Quantum many-body solver using artificial neural networks and its applications to strongly correlated electron systems

TL;DR

This paper discusses a novel quantum many-body solver that leverages artificial neural networks to analyze strongly correlated electron systems, enabling more accurate predictions of material properties and advancing computational materials science.

Contribution

It introduces a new neural network-based quantum many-body solver that improves quantitative predictions for strongly correlated electron systems.

Findings

Enhanced accuracy in predicting material-dependent properties

Integration of machine learning with first-principles calculations

Potential for advancing materials design and discovery

Abstract

With the evolution of numerical methods, we are now aiming at not only qualitative understanding but also quantitative prediction and design of quantum many-body phenomena. As a novel numerical approach, machine learning techniques have been introduced in 2017 to analyze quantum many-body problems. Since then, proposed various novel approaches have opened a new era, in which challenging and fundamental problems in physics can be solved by machine learning methods. Especially, quantitative and accurate estimates of material-dependent physical properties of strongly correlated matter have now become realized by combining first-principles calculations with highly accurate quantum many-body solvers developed with the help of machine learning methods. Thus developed quantitative description of electron correlations will constitute a key element of materials science in the next generation.