Elf autoencoder: unsupervised exploration of flat-band materials using electronic band structure fingerprints

TL;DR

This paper introduces an unsupervised autoencoder that analyzes electronic band structure images to identify and cluster flat-band materials, accelerating discovery in vast chemical spaces.

Contribution

The novel autoencoder framework enables autonomous exploration and clustering of flat-band materials based on electronic features, beyond traditional chemical classifications.

Findings

Successfully clusters materials with similar electronic properties

Uncovers hidden chemical trends in flat-band materials

Facilitates rapid screening of candidate compounds

Abstract

Two-dimensional materials with flat electronic bands are promising for realizing exotic quantum phenomena such as unconventional superconductivity and nontrivial topology, but exploring their vast chemical space remains challenging. Here, we introduce an unsupervised convolutional autoencoder agent (elf) that operates on electronic band structure images and is capable of mapping band features and extracting the latent space representation as a fingerprint, enabling autonomous clustering of materials with common electronic properties beyond traditional chemical paradigms. Unsupervised visualisation of the latent space then helps to uncover hidden chemical trends and identify promising candidates based on similarities to well-studied exemplars. Our framework paves the way for the accelerated discovery of novel flat-band materials with desirable electronic characteristics. It complements high-throughput ab initio methods by rapidly screening candidates and guides further investigations into the mechanisms governing the emergence of flat-band physics. We believe the elf autoencoder will be a valuable tool for the autonomous discovery of previously unexplored flat-band materials, aiding in the unbiased identification of compounds with desirable electronic properties in vast 2D chemical space.