Simulating moir\'e quantum matter with neural network

TL;DR

This paper introduces a neural network-based variational method to accurately simulate strongly correlated moiré quantum materials, revealing complex phases like Wigner crystals and Mott insulators.

Contribution

The paper presents a novel neural network wavefunction approach for simulating many-electron moiré systems with improved accuracy and efficiency.

Findings

Identified a generalized Wigner crystal at n=1/3

Discovered a Mott insulator at n=1

Revealed a correlated insulator with magnetic moments at n=2

Abstract

Moir\'e materials provide an ideal platform for exploring quantum phases of matter. However, solving the many-electron problem in moir\'e systems is challenging due to strong correlation effects. We introduce a powerful variational representation of quantum states, many-body neural Bloch wavefunction, to solve many-electron problems in moir\'e materials accurately and efficiently. Applying our method to the semiconductor heterobilayer WSe2/WS2 , we obtain a generalized Wigner crystal at filling factor n = 1/3, a Mott insulator n = 1, and a correlated insulator with local magnetic moments and antiferromagnetic spin correlation at n = 2. Our neural network approach improves the simulation accuracy of strongly interacting moir\'e materials and paves the way for discovery of new quantum phases with variational learning principle in a unified framework.