High-Throughput Prediction of the Band Gaps of van der Waals Heterostructures via Machine Learning.

ABSTRACT: Van der Waals heterostructures offer an additional degree of freedom to tailor the electronic structure of two-dimensional materials, especially for the band-gap tuning that leads to various applications such as thermoelectric and optoelectronic conversions. In general, the electronic gap of a given system can be accurately predicted by using first-principles calculations, which is, however, restricted to a small unit cell. Here, we adopt a machine-learning algorithm to propose a physically intuitive descriptor by which the band gap of any heterostructures can be readily obtained, using group III, IV, and V elements as examples of the constituent atoms. The strong predictive power of our approach is demonstrated by high Pearson correlation coefficient for both the training (292 entries) an

SUBMITTER: Hu R 

PROVIDER: S-EPMC9268276 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature