Prediction of Oxygen Evolution Activity for Spinel Oxide via Machine Learning
Hydrogen production through water electrolysis has become a prominent technology for green hydrogen energy.The development of efficient electrocatalysts for the oxygen evolution reaction(OER)is crucial for this process.Spinel oxide,known for their low overpotential and long-term stability,have been extensively investigated as cathode materials for alkaline OER.However,it remains a great challenge to search for high performance spinel oxide using trial and error approaches in a reasonable timescale from a large number of possible candidates.To address these challenges,this study proposes a machine learning-based approach for predicting the OER performance of spinel-type catalysts.The support vector machine(SVM)algorithm is utilized to establish a relationship between the catalyst's chemical composition(AxByCzO4),synthesis method,calcination temperature,calcination time,calcination atmosphere,heating rate,morphology,electrolyte,catalyst loading,working electrode,and overpotential.The model achieved an average mean squared error of 182.7 and an average absolute error of 20.6,indicating its effectiveness in predicting catalyst performance.This approach provides an efficient method for the development of spinel-type OER catalyst materials.
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