Electronic crystals and quasicrystals in semiconductor quantum wells: an AI-powered discovery

TL;DR

This paper introduces an AI-driven neural network approach to study quantum wells, revealing new electronic phases including metallic, crystalline, and a novel quasicrystal phase in semiconductor systems.

Contribution

The work develops a first-principles neural network variational method to uncover complex electronic phases in quantum wells, including the discovery of an electronic quasicrystal.

Findings

Revealed metallic and crystalline phases in quantum wells.

Discovered a new electronic quasicrystal phase.

Demonstrated the effectiveness of AI in condensed matter discovery.

Abstract

The homogeneous electron gas is a cornerstone of quantum condensed matter physics, providing the foundation for developing density functional theory and understanding electronic phases in semiconductors. However, theoretical understanding of strongly-correlated electrons in realistic semiconductor systems remains limited. In this work, we develop a neural network based variational approach to study quantum wells in three dimensional geometry for a variety of electron densities and well thicknesses. Starting from first principles, our unbiased AI-powered method reveals metallic and crystalline phases with both monolayer and bilayer charge distributions. In the emergent bilayer, we discover a new quantum phase of matter: the electronic quasicrystal.