Statistical Modeling of Frictional Properties: a Machine Learning Approach

Controlling frictional forces is one of the major challenges in addressing the tribological response of interfaces. Besides, interpreting electronic features with microscopic tribological properties could provide a route to design lubricant additives. However, explicit evaluation of the frictional and adhesive properties of the interface is quite challenging since it requires expensive computational and experimental approaches. Consequently, statistical machine learning (ML) models have been developed to predict the adhesive, cohesive, and Van der Waals interaction energy. Using first principle methods, different stacking orders of structurally stable monolayers from the c2db database are simulated to generate the lowest energy structure of 760 bilayers. Utilizing the least absolute shrinkage and selection operator (LASSO), structural and interface elemental properties of these bilayers are identified as the potential descriptors for predicting the response variables. Different regression models have been developed using the selected descriptors, and their accuracies are compared to find the best-optimized model for predicting the frictional energies of bilayers. The high coefficient of determination (R2) and lower root mean square error (rmse) of the best ML model give the statistical interplay between the frictional force and elementary properties. Additionally, SHapley Additive exPlanations (SHAP) values have been used to interpret the selected descriptors and ML models.