Machine Learning-Assisted Development of Platinum-Free RuNiCo Nanocages for Electrocatalytic Hydrogen Oxidation Reaction in Acidic Media
Through data-driven synthesis involving 24 experiments from 16,384 possible combinations (<1%), we developed two optimal electrocatalysts for the hydrogen oxidation reaction (HOR) in acidic media: PtIrRuNiCo nanowires and Pt-free RuNiCo nanocages. The objective function obtained in the Bayesian optimization (BO) process is highly correlated with the experimental current density for the HOR. The RuNiCo nanocages exhibited higher HOR activity than Ru because of the suppression of the surface oxide formation. Our BO-assisted approach accelerates the development of highly active electrocatalysts without requiring high-throughput synthesis.
Authors
- Ichizo Yagi (ORCID: https://orcid.org/0000-0002-4005-9440)
- Masaru Kato (ORCID: https://orcid.org/0000-0002-2845-3525)
- Takeshi Watanabe (ORCID: https://orcid.org/0000-0001-7415-0181)
- Colin A. Tadgell (ORCID: https://orcid.org/0000-0003-4384-9744)
- Sae Dieb (ORCID: https://orcid.org/0000-0002-8111-2009)
- Keitaro Sodeyama (ORCID: https://orcid.org/0000-0002-9228-0729)
- Bohao Du
- Koshiro Suzuki (ORCID: https://orcid.org/0000-0001-8190-4957)
- Toshihiko Hoshi
Institutions
- Hokkaido University (JP)
- National Institute for Materials Science (JP)
- Japan Synchrotron Radiation Research Institute (JP)
- Shanghai Industrial Technology Institute (CN)
Publication Details
- Journal
- ACS Applied Energy Materials
- Published
- 2025-09-29
- DOI
- https://doi.org/10.1021/acsaem.5c02113
- Citations
- 2
- Primary Topic
- Electrocatalysts for Energy Conversion
- Type
- article
- Field-Weighted Citation Impact
- 0.73