Impressive computational acceleration by using machine learning for 2-dimensional super-lubricant materials discovery

TL;DR

This paper presents a machine learning method that efficiently predicts structural properties of 2D layered materials, significantly reducing computational costs while maintaining high accuracy for discovering super-lubricant materials.

Contribution

The authors develop a machine learning approach to rapidly estimate properties of layered 2D materials, enabling faster discovery of super-lubricants compared to traditional methods.

Findings

Machine learning models accurately predict interlayer energy and elastic constants.

The approach reduces computational time and resources significantly.

High correlation with density functional theory results.

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 be often extremely time consuming. We describe a time and resource-efficient machine learning approach to create a large dataset of structural properties of van der Waals layered structures. In particular, we focus on the interlayer energy and the elastic constant of layered materials composed of two different 2-dimensional (2D) structures, that are important for novel solid lubricant and super-lubricant materials. We show that machine learning models can recapitulate results of computationally expansive approaches (i.e. density functional theory) with high accuracy.