Korean Journal of Materials Research (한국재료학회지)

Materials Research Society of Korea (한국재료학회)
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Can Artificial Intelligence Boost Developing Electrocatalysts for Efficient Water Splitting to Produce Green Hydrogen?

  • Jaehyun Kim (Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University) ;
  • Ho Won Jang (Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University)
  • Received : 2022.12.09
  • Accepted : 2022.12.30
  • Published : 2023.05.27
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Abstract

Water electrolysis holds great potential as a method for producing renewable hydrogen fuel at large-scale, and to replace the fossil fuels responsible for greenhouse gases emissions and global climate change. To reduce the cost of hydrogen and make it competitive against fossil fuels, the efficiency of green hydrogen production should be maximized. This requires superior electrocatalysts to reduce the reaction energy barriers. The development of catalytic materials has mostly relied on empirical, trial-and-error methods because of the complicated, multidimensional, and dynamic nature of catalysis, requiring significant time and effort to find optimized multicomponent catalysts under a variety of reaction conditions. The ultimate goal for all researchers in the materials science and engineering field is the rational and efficient design of materials with desired performance. Discovering and understanding new catalysts with desired properties is at the heart of materials science research. This process can benefit from machine learning (ML), given the complex nature of catalytic reactions and vast range of candidate materials. This review summarizes recent achievements in catalysts discovery for the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). The basic concepts of ML algorithms and practical guides for materials scientists are also demonstrated. The challenges and strategies of applying ML are discussed, which should be collaboratively addressed by materials scientists and ML communities. The ultimate integration of ML in catalyst development is expected to accelerate the design, discovery, optimization, and interpretation of superior electrocatalysts, to realize a carbon-free ecosystem based on green hydrogen.

Keywords

Acknowledgement

This study was supported by the KRISS (Korea Research Institute of Standards and Science) MPI Lab. Program and the National Research Foundation of Korea (NRF) grant funded by the Korea Government MSIT (2021R1C1C2006142). The Inter-university Semiconductor Research Center and Institute of Engineering Research at Seoul National University provided research facilities for this study.

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