  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.
