Recent Progress in Predicting Corrosion Evolution and Degradation of Aluminum Alloys by Multiscale Simulation Frameworks

As the second most widely used metallic material after steel, research on the corrosion resistance of aluminum (Al) alloys holds great significance. To overcome the inherent limitations of experimental methods in capturing long‐term multifactor corrosion behavior of Al alloys, this review summarizes recent advancements in the multiscale simulation of their corrosion behavior. Although previous reviews have addressed individual simulation techniques or specific corrosion phenomena, a framework linking model capability, scale transfer, alloy characteristics, and engineering applicability remains limited. This review provides a systematic comparison of representative coupled multiscale frameworks in terms of their applicable scales, input parameters, output capabilities, advantages, and limitations. First, an overview of various Al alloy series is provided, highlighting their specific material characteristics and corrosion failure modes. Subsequently, simulation methodologies across distinct scales, ranging from the atomic level for unveiling corrosion thermodynamics and kinetics, to mesoscopic and macroscopic scales for predicting morphological evolution and performance degradation, are summarized. After that, multiphysics and multiscale simulation strategies, focusing on data transfer methods between different scales, are explored. Specifically, detailed descriptions of density functional theory (DFT)‐molecular dynamics (MD), MD‐phase field (PF), finite element method (FEM)‐machine learning (ML), and DFT‐MD‐ML coupled models are provided. Finally, engineering applications and future prospects are discussed, emphasizing the necessity of overcoming challenges in data transfer and highlighting the potential of artificial intelligence in achieving intelligent corrosion management.