Machine Learning Guided Dopant Selection for Metal Oxide‐Based Photoelectrochemical Water Splitting: The Case Study of Fe₂O₃ and CuO

Abstract Doping is an effective strategy for tuning metal oxide‐based semiconductors for solar‐driven photoelectrochemical (PEC) water splitting. Despite decades of extensive research effort, the dopant selection is still largely dependent on a trial‐and‐error approach. Machine learning (ML) is promising in providing predictable insights on the dopant selection for high‐performing PEC systems because it can uncover correlations from the seemingly ambiguous linkages between vast features of dopants and the PEC performance of doped photoelectrodes. Herein, the authors successfully build ML model to predict the doping effect of 17 metal dopants into hematite (Fe₂O₃), a prototype photoelectrode material. Their findings disclose the critical parameters from the 10 intrinsic features of each dopant. The model is further experimentally validated by the coherent prediction on Y and La dopants’ behaviors. Further interpretation of the ML model suggests that the chemical state is the most significant selection criteria, meanwhile, dopants with higher metal–oxygen bond formation enthalpy and larger ionic radius are favored in improving the charge separation and transfer (CST) in the Fe₂O₃ photoanodes. The generic feature of this ML guided selection criteria has been further extended to CuO‐based photoelectrodes showing improved CST by alkaline metal ions doping.