Prediction of Oxygen Evolution Activity for NiCoFe Oxide Catalysts via Machine Learning

Transition metal (such as Fe, Co, and Ni) oxides are excellent systems in the oxygen evolution reaction (OER) for the development of non-noble-metal-based catalysts. However, direct experimental evidence and the physical mechanism of a quantitative relationship between physical factors and oxygen evolution activity are still lacking, which makes it difficult to theoretically and accurately predict the oxygen evolution activity. In this work, a data-driven method for the prediction of overpotential (OP) for (Ni-Fe-Co)O