Machine Learning Strategies to Circumvent Variabilities in Chemical Vapour‐Deposited Graphene Coatings for Remarkable Corrosion Resistance of Metals

Graphene coatings, derived through chemical vapour deposition (CVD), offer a promising solution for corrosion mitigation due to their unique combination of impermeability, inertness and toughness. While pristine graphene coatings significantly enhance corrosion resistance, variabilities in the properties and the performance of these coatings, when influenced by CVD process parameters, pose a challenge. Variations in defect content, largely due to differing CVD methodologies across research groups, significantly affect corrosion resistance, ranging from notable improvements to potential acceleration of corrosion. To systematically address these variabilities, this review proposes the integration of machine learning (ML) techniques to optimise CVD parameters, thereby achieving more consistent and high‐quality graphene coatings. Moreover, recent advancements in artificial intelligence, specifically large language models (LLMs), have the potential to revolutionise this domain by enabling sophisticated data analysis and hypothesis generation from vast quantities of literature. These capabilities can accelerate the optimisation of graphene production processes. By integrating LLMs, researchers can quickly synthesise and assess vast amounts of published data, refine process parameters and predict outcomes more accurately. This approach not only enhances the understanding of CVD graphene's behaviour under different conditions but also drives the innovation of robust methodologies for the development of corrosion‐resistant coatings.