The screening of novel materials is an important topic in the field of materials science. Although traditional computational modeling, especially first-principles approaches, is a very useful and accurate tool to predict the properties of novel materials, it still demands extensive and expensive state-of-the-art computational resources. Additionally, they can often be extremely time consuming. A time and resource efficient machine learning approach to create a dataset of structural properties of 18 million van der Waals layered structures is described. In particular, the authors focus on the interlayer energy, the elastic constant, and band gap of layered materials composed of two different 2D structures. It is shown that machine learning models can predict results of computationally expansive approaches (i.e., density functional theory) with high accuracy.

Active learning for functional and structural properties prediction of novel Van der Waals hetero-structures

ACS Spring 2022

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.