Sparse Representation For Machine Learning The Properties Of Defects In 2d Materials

Two-dimensional materials offer a promising platform for the next generation of (opto-) electronic devices and other high technology applications. We propose a machine learning approach for rapid estimation of the properties of 2D material given the lattice structure and defect configuration. This method suggests a way to represent configuration of 2D materials with defects that allows a neural network to train quickly and accurately. We compare our methodology with the state-of-the-art approaches and demonstrate at least 3.7 times energy prediction error drop. Atomic-scale tailoring of materials is one of the most promising paths towards achieving new, both quantum and classical properties.