Unraveling the Atomistic Mechanisms Underlying the Antiwear Function of the ZDDP Lubricant Additive by Machine-Learning-Informed Molecular Dynamics.

Authors

Enrico Pedretti, Francesca Benini, Giovanni Gravili, Huong Thi Thuy Ta, Maria Clelia Righi

Published in

ACS applied materials & interfaces. Aug 25, 2026. Epub Aug 25, 2026.

Abstract

Wear is a major challenge in mechanical systems, particularly in engines, where it compromises efficiency and component lifetime. For over 80 years, zinc dialkyldithiophosphate (ZDDP) has been the industry-standard antiwear additive, but increasing environmental regulations now demand suitable replacements-a goal that requires a precise understanding of its protective mechanism. While experiments have established the composition of ZDDP-derived tribofilms, the atomistic pathways driving their growth remain unclear due to the difficulty of simulating highly reactive tribochemical environments at realistic time and length scales with the required chemical accuracy. Leveraging recent advances in machine-learning potentials (MLPs), we develop the first MLP specifically tailored for ZDDP and apply it to large-scale molecular dynamics simulations of tribofilm growth at iron interfaces with varying reactivities, including oxidized surfaces and nano-asperities. We show that C-O bond cleavage is the rate-limiting step in the formation of the polyphosphate component of the tribofilm and that linkage isomers obtained by S-to-O substitution could act as reactive intermediates that accelerate the development of the characteristic polyphosphate network. We also identify distinct decomposition mechanisms to form hydrocarbon by-products, a key step to obtaining carbon-free tribofilms. These simulations provide the first atomistically resolved, dynamic picture of ZDDP tribofilm formation on ferrous interfaces, offering novel insights into several open questions and paving the way for future simulations that can guide the design of sustainable next-generation antiwear additives.

PMID:
42673570
Bibliographic data and abstract were imported from PubMed on 01 Sep 2026.

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