Emergent Wigner phases in moir\'e superlattice from deep learning

TL;DR

This paper introduces an unsupervised deep learning method to identify and analyze emergent electronic phases, including novel Wigner crystal states, in moiré superlattices, overcoming computational challenges of traditional techniques.

Contribution

The authors develop a general, efficient deep learning framework to uncover complex quantum phases in moiré systems, revealing new Wigner crystal states and emphasizing the importance of spin polarization.

Findings

Identified diverse quantum states, including novel Wigner phases.

Revealed the role of spin polarization in Wigner phase determination.

Provided a scalable deep learning approach for moiré physics studies.

Abstract

Moir\'e superlattice designed in stacked van der Waals material provides a dynamic platform for hosting exotic and emergent condensed matter phenomena. However, the relevance of strong correlation effects and the large size of moir\'e unit cells pose significant challenges for traditional computational techniques. To overcome these challenges, we develop an unsupervised deep learning approach to uncover electronic phases emerging from moir\'e systems based on variational optimization of neural network many-body wavefunction. Our approach has identified diverse quantum states, including novel phases such as generalized Wigner crystals, Wigner molecular crystals, and previously unreported Wigner covalent crystals. These discoveries provide insights into recent experimental studies and suggest new phases for future exploration. They also highlight the crucial role of spin polarization in determining Wigner phases. More importantly, our proposed deep learning approach is proven general and efficient, offering a powerful framework for studying moir\'e physics.