Machine Learning Guided Dopant Selection for Metal Oxide‐Based Photoelectrochemical Water Splitting: The Case Study of Fe<sub>2</sub>O<sub>3</sub>and CuO

  • Zhiliang Wang
    School of Chemical Engineering the University of Queensland St Lucia Queensland 4072 Australia
  • Yuang Gu
    School of Chemical Engineering the University of Queensland St Lucia Queensland 4072 Australia
  • Lingxia Zheng
    State Key Laboratory Breeding Base of Green Chemistry Synthesis Technology Department of Applied Chemistry Zhejiang University of Technology Hangzhou 310032 P. R. China
  • Jingwei Hou
    School of Chemical Engineering the University of Queensland St Lucia Queensland 4072 Australia
  • Huajun Zheng
    State Key Laboratory Breeding Base of Green Chemistry Synthesis Technology Department of Applied Chemistry Zhejiang University of Technology Hangzhou 310032 P. R. China
  • Shijing Sun
    Department of Mechanical Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA
  • Lianzhou Wang
    School of Chemical Engineering the University of Queensland St Lucia Queensland 4072 Australia

抄録

<jats:title>Abstract</jats:title><jats:p>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 (Fe<jats:sub>2</jats:sub>O<jats:sub>3</jats:sub>), 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 Fe<jats:sub>2</jats:sub>O<jats:sub>3</jats:sub>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.</jats:p>

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