machine learning Tag

Temperature plays a crucial role in electrodeposition processes, influencing both the kinetics and thermodynamics of the reaction. As temperature increases, the rate of electrodeposition generally accelerates due to enhanced mass transport of ions to the electrode surface and increased reaction kinetics. Additionally, higher temperatures often lead to changes in the morphology, structure, and composition of the deposited material, affecting its properties such as adhesion, density, and crystallinity. However, the effect of temperature on electrodeposition can be complex, as excessive heat may also promote side reactions, electrolyte decomposition, or changes in the electrode surface, potentially leading to poor-quality deposits or altered electrochemical behavior. Thus, optimizing temperature conditions is essential for controlling the quality, uniformity, and properties of electrodeposited coatings and films.   Through collaboration with our partners and leveraging machine learning (ML) techniques, we identified the optimal temperature for electrodeposition processes. Subsequently, we embarked on physics-based modeling efforts to understand the intricate relationship...