Advancing tribological simulations of carbon-based lubricants with active learning and machine learning molecular dynamics

The need to move toward more sustainable lubricant materials has sparked an ever growing interest on the tribological performances of additives based on environmentally friendly molecules, such as carbon-based compounds, and green liquid media as aqueous solutions. The prediction of the solubility of the additives into the liquid and the tribochemistry of decomposition and polymerization of the additive molecules under harsh conditions is essential for understanding the atomistic mechanisms leading to the formation in situ of the carbon-based lubricious tribofilms so effective in reducing friction and wear at solid interfaces. To this extent, the application of tools like ab initio molecular dynamics based on first-principle density functional theory is severely hindered by the size of the systems of interests and the need to simulate their dynamics over relatively long times. To enable tribological simulations with quantum accuracy for a first time, we develop a workflow for smart configuration sampling in active learning, to obtain machine learning interatomic potentials which are shown to be sufficiently robust and accurate also in the characteristic harsh conditions generated by high loads and shear rates. Focusing on glycerol rich lubricants, we apply this active learning strategy to generate a neural network potential to simulate the formation and behavior of nanometer thick molecular tribofilms. The simulations reveal the superior accuracy of the machine learning approach with respect to classical molecular dynamics with reactive force fields, and pave the way for more promising in depth exploration of novel carbon-based lubricants.