Ab initio informed machine learning potential for tribochemistry and mechanochemistry: Application for eco–friendly gallate lubricant additive

Abstract

Mechanochemistry and tribochemistry processes involve multiple physical/chemical interactions induced by extreme conditions including molecular confinement, high temperatures and mechanical stress applied. Simulating these processes by molecular dynamics is very challenging. While force fields fall short reproducing the enhanced reactivity arising by quantum effects, ab initio molecular dynamics is severely limited by the complexity of the systems of interest, their sizes, and the long-time scale on which relevant events take place. In this work using an active learning approach, a landmark deep neural network potential has been developed which reproduces the accuracy of ab initio interactions at the classical molecular dynamics computational cost and permits to successfully simulate the tribochemical processes occurring at the interface between butyl gallate molecules and iron substrates under tribological conditions. The simulations of the dynamics of the Fe-gallate system when sliding under an imposed external load reveal the key atomistic mechanisms underlying the formation of the friction reducing lubricant tribofilm and permit to characterize the tribological properties of the explored systems, clearly exposing the shortcoming of reactive force field based approaches. The successful development of neural network potentials, making it possible to push the limits of molecular dynamics marrying accuracy with system sizes and long time scales, paves the route toward a new area in computational tribochemistry.