  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.
