Another limitation is that many DFT studies overlook the potential dependence of the corrosion process, which is a crucial factor under realistic conditions. The variation in potentials which results from natural electrochemical gradients between anodic and cathodic regions, can significantly influence properties such as adsorption energies, reaction pathways, and stability of corrosion products, leading to different corrosion behaviours. Without accounting for these potential dependencies, simulations often unable to accurately predict real-world corrosion processes and mechanisms. Despite this importance, there is a scarcity of DFT studies addressing these effects. The recent development of constant potential simulation techniques, such as grand canonical DFT, along with robust implicit solvation methods and the hybrid use of implicit and explicit solvation techniques, offers a promising avenue to revisit these mechanisms in a more realistic manner. In addition, the corrosion process is dynamic and slow in nature, which limits the realistic time scales in molecular simulations. On the other hand, lack of reliable interatomic potentials for multi-element alloy-environment systems restricts the ability to accurate modelling the process. Moreover, existing parameterised interatomic force fields limit the potential to capture complex interactions and species involved in the corrosion process. While AIMD offers more reliability in capturing these complexities in systems, their high computational cost makes AIMD methods impractical for many corrosion studies. These issues may be overcome, to some extent, by using machine learning (ML) and artificial intelligence (AI) techniques. For example, ML-accelerated MD simulations can significantly enhance the time scale, allowing for more comprehensive investigations of the corrosion process. Moreover, the use of deep neural networks for generating on-the-fly interatomic potentials can provide highly reliable and accurate models for systems. AI algorithms-derived predictive models can be used to understand long term corrosion behaviour based on short term simulation data. The reliability of the simulation results can be enhanced by validating the simulation data against experimental data, leading to continuous improvement.