Atomistic Modeling of Processing–Structure–Property Correlations in Thin Films: A Critical Review

Advances in thin films are crucial for progress across electronics, optics, energy systems, coatings, and devices, where performance is governed by the strong coupling between processing conditions, microstructure, and material properties. This review provides an overview of atomistic and multiscale modeling approaches to elucidate processing–structure–property correlations in thin films. Key deposition techniques, including physical and chemical vapor deposition, are discussed alongside postdeposition treatments influencing crystallinity, defects, residual stress, and adhesion. Density functional theory (DFT), molecular dynamics (MD), Monte Carlo (MC), and kinetic MC (KMC) simulation techniques are reviewed in the context of electronic structure, atomic‐scale dynamics, growth kinetics, and microstructural evolution. Emphasis is placed on how these methods, individually and synergistically, reveal mechanisms of nucleation, diffusion, defect formation, stress development, and interfacial mixing. Recent advances in hybrid multiscale frameworks, including DFT‐informed KMC and MD/KMC schemes, are highlighted for their ability to bridge disparate length and time scales. Recent progress in machine learning–assisted simulations, autonomous deposition optimization, and physics‐informed neural networks transforming predictive modeling of thin film growth is presented. Finally, structural descriptors, defect characterization, interfacial adhesion metrics, and mechanical, thermal, electrical, and optical property predictions are critically assessed, enabling the design of high‐performance thin films.