Review Article

Molecular dynamics simulations of thin film and coatings: a review

¹ GREMI, UMR7344, CNRS-Université d’Orléans, 14 rue d’Issoudun, F-45067 Orléans, France
² MS4ALL, 1 avenue du Champ de Mars, F-45100 Orléans, France

^* emails: This email address is being protected from spambots. You need JavaScript enabled to view it. ; This email address is being protected from spambots. You need JavaScript enabled to view it.

Received: 26 March 2026
Accepted: 9 June 2026
Published online: 14 July 2026

Abstract

The synthesis of thin films and coatings constitutes a cornerstone of modern materials science, underpinning technologies ranging from semiconductor logic gates to tribological barriers and catalytic membranes. Over the last decades, the need for precise control over film microstructure has driven a transition towards highly energetic processes, such as High Power Impulse Magnetron Sputtering (HiPIMS), and chemically complex methods like Atomic Layer Deposition (ALD) and solution-based processing. While continuum models effectively classify growth regimes based on macroscopic parameters, they inherently lack the resolution to describe the discrete atomic assembly processes—nucleation, island coalescence, and defect formation—that define functional performance. This review presents a comprehensive survey of Molecular Dynamics (MD) simulations applied to thin film growth. We detail the evolution of interatomic potentials, from classical descriptions (EAM, Tersoff) to reactive force fields (ReaxFF) and emerging Machine Learning Potentials (MLIP). The review critically analyzes MD contributions to understanding both organic systems (plasma polymers, self-assembled monolayers, solvent evaporation) and inorganic coatings (metals, complex oxides, high-entropy alloys). We demonstrate how MD bridges the gap between plasma physics and materials engineering, offering predictive scaling laws for density, intrinsic stress, and roughness. Furthermore, we address the timescale limitations of classical MD by reviewing hybrid multiscale frameworks coupling MD with Kinetic Monte Carlo (KMC) and Finite Element Methods (FEM), and highlight emerging applications in energy storage (solid-electrolyte interphase) and biomedical interfaces.