Researchers are exploring the use of deep learning approaches to study the genome of two-dimensional materials with flat electronic bands. Various machine learning methods, including symmetry-based clustering and physically informed algorithms, are being developed to accelerate the discovery of new materials and structures. These approaches are being supported by open-source infrastructure and computational databases, such as 2DMatPedia, to facilitate advanced manufacturing and materials discovery.