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This thesis explores the use of machine learning to optimize metal-doping in nickel oxide (NiO) for enhancing anodic oxidation reactions, specifically targeting the oxygen evolution reaction (OER). A machine learning framework was developed using a dataset of 1,132 points to predict the effects of various dopants, revealing that iron doping significantly improves performance. The findings are validated against experimental data, and the models are made accessible through an interactive web application for r

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hg951146

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EN