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

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 complexityofthesystemsofinterest,theirsizes,andthelong-timescaleonwhichrelevanteventstakeplace.In this work using an active learning approach, a landmark deep neural network potential has been developed which reproduces the accuracy of abinitiointeractions at the classical molecular dynamics computational cost andpermitstosuccessfullysimulatethetribochemicalprocessesoccurringattheinterfacebetweenbutylgallate 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 for- mation 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 developmentofneuralnetworkpotentials,makingitpossibletopushthelimitsofmoleculardynamicsmarrying accuracy with system sizes and long time scales, paves the route toward a new area in computational tri- bochemistry.