High Throughput Screening of Millions of van der Waals Heterostructures for Superlubricant Applications. Issue 11 (9th September 2020) - Fronzi, Marco; Tawfik, Sherif Abdulkader; Ghazaleh, Mutaz Abu; Isayev, Olexandr; Winkler, David A.; Shapter, Joe; Ford, Michael J.
Abstract: 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 and the elastic constant of layered materials composed of two different 2D structures that are important for novel solid lubricant and super‐lubricant materials. It is shown that machine learning models can predict results of computationally expansive approaches (i.e., density functional theory) with high accuracy. Abstract : A combined density functional theory and machine learning approach is employed to create a very large dataset of structural properties (i.e., interlayer energy and the elastic constant) of van der Waals layered materials. These properties are important for novel solid lubricant and super‐lubricant materials discovery.