Phosphorus-based lubricant additives on iron with Machine Learning Interatomic Potentials

Phosphorus-based lubricant additives protect metallic contacts under boundary lubrication by forming surface films that reduce wear and friction. However, the molecular mechanisms driving their friction-reducing effects remain unclear, especially for phosphate esters, whose molecular structure critically impacts tribological behavior. Here, we employ machine learning–based molecular dynamics simulations to investigate the tribological performance of three representative phosphorus-based additives, Dibutyl Hydrogen Phosphite (DBHP), Octyl Acid Phosphate (OAP), and Methyl Polyethylene Glycol Phosphate (mPEG-P), confined between iron surfaces. DBHP exhibits the lowest friction and largest interfacial separation, combining strong surface reactivity with significant steric hindrance. In contrast, phosphate-based additives show higher friction due to limited steric protection and partial loss of surface coverage under extreme conditions. Systematic variation within the mPEG-P series reveals that increasing ester functionality and chain length reduces friction by enhancing steric separation, even when surface reactivity decreases. These results establish a mechanistic hierarchy in boundary lubrication, where chemical reactivity promotes film formation, while steric architecture primarily controls shear response. These findings provide atomistic guidelines for the rational design of phosphorus-based lubricant additives that balance reactive anchoring with optimized steric structures. • Machine learning MD reveals friction mechanisms of phosphate additives. • DBHP outperforms OAP and mPEG-P in boundary lubrication. • Limited steric protection leads to friction increase in OAP and mPEG-P monoester. • Steric effects can dominate over surface reactivity in boundary lubrication.