Machine learning approach to genome of two-dimensional materials with flat electronic bands

TL;DR

This paper introduces a machine learning framework combining supervised and unsupervised methods to identify and classify 2D materials with flat electronic bands, revealing new material classes for studying electron interactions.

Contribution

A novel hybrid machine learning approach automates the discovery and classification of 2D flat band materials, expanding the known material landscape.

Findings

Identified new classes of 2D flat band materials.

Automated classification accelerates materials discovery.

Revealed previously unknown material systems for electronic interaction studies.

Abstract

Many-body physics of electron-electron correlations plays a central role in condensed mater physics, it governs a wide range of phenomena, stretching from superconductivity to magnetism, and is behind numerous technological applications. To explore this rich interaction-driven physics, two-dimensional (2D) materials with flat electronic bands provide a natural playground thanks to their highly localised electrons. Currently, thousands of 2D materials with computed electronic bands are available in open science databases, awaiting such exploration. Here we used a new machine learning algorithm combining both supervised and unsupervised machine intelligence to automate the otherwise daunting task of materials search and classification, to build a genome of 2D materials hosting flat electronic bands. To this end, a feedforward artificial neural network was employed to identify 2D flat band materials, which were then classified by a bilayer unsupervised learning algorithm. Such a hybrid approach of exploring materials databases allowed us to reveal completely new material classes outside the known flat band paradigms, offering new systems for in-depth study on their electronic interactions.