Prediction of Nanoscale Friction for Two-Dimensional Materials Using a Machine Learning Approach

Abstract

Several two-dimensional (2D) materials such as graphene, molybdenum disulfide, or boron nitride are emerging as alternatives for lubrication additives to control friction and wear at the interface. On the other hand, the initiative to accelerate materials discovery through data-driven computational methods has identified numerous novel topologies and families of 2D materials that can potentially be designed as low-friction additives. Hence, generating a structure–property (friction) correlations for 2D material-based additives that present a large variation in atomic composition is the next big challenge. Herein, we present a machine learning (ML) method using the Bayesian modeling and transfer learning approach to predict the maximum energy barrier (MEB) of the potential surface energy (correlated to intrinsic friction) of ten different 2D materials that were previously unexplored for their tribological properties. The descriptors (or properties) required to train the ML model with high accuracy are identified by taking into account the established physical models for dissipation in 2D materials. As a result, a difference of less than 8% in MEB values as predicted via the ML model presented here and the PES profiles generated using molecular dynamics simulations, for a select few 2D materials, was obtained. The model also enabled the identification of material properties that present the highest sensitivity to the corrugated potential, hence enabling the development of design routes for the synthesis of 2D materials with optimal tribological properties.