Theoretical Prediction of the Electronic and Structural Properties of Van der Waals Heterostructures Using a Combined Machine Learning and Density Functional Theory Approach

Van der Waals (vdW) heterostructures are made of different two-dimensional (2D) monolayers vertically stacked and weakly coupled by van der Waals forces. VdW heterostructures often possess rich physical and chemical properties that are unique to their constituent monolayers. As many 2D materials have been recently identified, the combinatorial configuration space of vdW stacked heterostructures grows exceedingly large, making it difficult to explore through traditional experimental or computational approaches in a trial-and-error manner. Here we present a computational framework combining first-principles electronic structure calculation, 2D materials database, and supervised machine learning approach to construct efficient data-driven models capable of predicting the properties of vdW heterostructures from the properties of their constituent monolayers We apply this approach to predict the band gap, band edges, interlayer distance, and interlayer binding energy of vdW heterostructures. Our data-driven model will open avenues for efficient screening and discovery of low-dimensional vdW heterostructures with desired electronic and optical properties for targeted device applications.