Band gap predition of very large number of novel Van der Waals heterostructures using active learing models

The band gap is one of the most fundamental properties of condensed matter. However, an accurate calculation of its value, which could potentially allow experimentalists to identify suitable materials for device applications, is very computationally expensive. Here, active machine learning algorithms are used to leverage a limited number of accurate density functional theory calculations to robustly predict the band gap of a very large number of novel heterostructures. Using this approach, a database of approximately 2.2 million band gap values for various novel Van der Waals heterostructures has been produced.