Phosphorus-based lubricant additives on iron with Machine Learning Interatomic Potentials
Paolo Restuccia, Enrico Pedretti, Francesca Benini, Sophie Loehl\'e, M. Clelia Righi

TL;DR
This study uses machine learning-based molecular dynamics to analyze how molecular structure influences the tribological performance of phosphorus-based lubricant additives on iron, providing insights for designing more effective low-friction lubricants.
Contribution
It introduces a machine learning approach to simulate and understand the molecular mechanisms of phosphorus-based additives on iron surfaces, highlighting the role of steric effects and surface reactivity.
Findings
DBHP shows lowest friction due to steric hindrance and reactivity.
OAP and mPEG-P have higher friction with partial surface coverage.
Longer-chain mPEG-P reduces friction by maintaining larger separations.
Abstract
Phosphorus-based lubricant additives are used for protecting metallic contacts under boundary lubrication by forming surface films that reduce wear and friction. Despite their importance, the molecular mechanisms driving their friction-reducing effects remain unclear, especially for phosphate esters, whose molecular structure critically impact tribological behavior. In this study, we use 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), on iron surfaces. The mPEG-P family is further analyzed by varying esterification degree and chain length. DBHP exhibits the lowest friction and largest interfacial separation, resulting from steric hindrance and tribochemical reactivity, as…
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Taxonomy
TopicsLubricants and Their Additives · Tribology and Wear Analysis · Adhesion, Friction, and Surface Interactions
