Computational experiments of metal corrosion studies: A review
Shuhao Li, Chunqing Li, Feng Wang
Abstract
This review article underscores the critical role of Density Functional Theory (DFT) in the prediction of corrosion defect structures based on specific chemical compositions. By integrating DFT with Molecular Dynamics (MD) simulations, we gain a more nuanced understanding of corrosion processes. The article further explores how advanced computational approaches, encompassing DFT calculations, MD simulations, and the innovative application of Machine Learning (ML) and Artificial Intelligence (AI), are revolutionizing corrosion studies. These technologies enhance our ability to comprehend and predict the progression of corrosion defect depth across various environments. ML and AI algorithms are particularly noted for their capacity to identify complex patterns, thereby enabling the development of more accurate predictive models for corrosion behavior. As computational resources continue to evolve, leveraging high-performance computing has become pivotal for simulating larger systems and achieving more detailed insights. The convergence of quantum mechanics, molecular dynamics, and artificial intelligence marks a promising frontier for computational experiments in corrosion research, offering profound implications for maintenance strategies and the protection of critical infrastructure.