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

Abstract Electron-electron correlations play central role in condensed matter physics, governing phenomena from superconductivity to magnetism and numerous technological applications. Two-dimensional (2D) materials with flat electronic bands provide natural playground to explore interaction-driven physics, thanks to their highly localized electrons. The search for 2D flat band materials has attracted intensive efforts, especially now with open science databases encompassing thousands of materials with computed electronic bands. Here we automate the otherwise daunting task of materials search and classification by combining supervised and unsupervised machine learning algorithms. To this end, convolutional neural network was employed to identify 2D flat band materials, which were then subjected to symmetry-based analysis using a bilayer unsupervised learning algorithm. Such hybrid approach of exploring materials databases allowed us to construct a genome of 2D materials hosting flat bands and to reveal material classes outside the known flat band paradigms.