# Machine Learning-Assisted Development of Platinum-Free RuNiCo Nanocages for Electrocatalytic Hydrogen Oxidation Reaction in Acidic Media

https://mdr.nims.go.jp/datasets/f5ef125b-78dc-41f0-9844-3c807990adba

## File

- [Bayesian_HOR_Manuscript_ver49.docx](https://mdr.nims.go.jp/filesets/d516d493-d6d7-4fd8-a180-238d69e7f220/download) ([Detail](https://mdr.nims.go.jp/filesets/d516d493-d6d7-4fd8-a180-238d69e7f220.md))

## Id

f5ef125b-78dc-41f0-9844-3c807990adba

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2025-12-26T05:28:12.079268Z

## Updated at

2026-04-30T03:01:11.491392Z

## Published at

2026-09-28T23:46:36.911567Z

## Doi



## First published url

https://doi.org/10.1021/acsaem.5c02113

## Date published

2025-10-13

## Recorded date published

2025-10-13

## Resource type

journal_article

## Manuscript type

accepted_manuscript

## Collection



## Title

- title: Machine Learning-Assisted Development of Platinum-Free RuNiCo Nanocages for
    Electrocatalytic Hydrogen Oxidation Reaction in Acidic Media
  title_type: original
  lang: en

## Description

- description: '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.'
  description_type: abstract
  lang: und

## Creator

- name: Colin A. Tadgell
  role: author
- name: Masaru Kato
  role: author
- name: Sae Dieb
  role: author
  orcid: https://orcid.org/0000-0002-8111-2009
  organization: National Institute for Materials Science
- name: Keitaro Sodeyama
  role: author
  orcid: https://orcid.org/0000-0002-9228-0729
  organization: National Institute for Materials Science
- name: Takahito Hoshi
  role: author
- name: Koshiro Suzuki
  role: author
- name: Bohao Du
  role: author
- name: Takeshi Watanabe
  role: author
- name: Ichizo Yagi
  role: author

## Contact agent



## Publisher

organization: American Chemical Society (ACS)

## Managing organization



## Keyword

- subject: Electrocatalysts
  schema: not_defined
- subject: Hydrogen oxidation reaction
  schema: not_defined
- subject: Bayesian optimization
  schema: not_defined
- subject: Polymer electrolyte fuel cell
  schema: not_defined

## Rights

- description: This document is the Accepted Manuscript version of a Published Work
    that appeared in final form in ACS Applied Energy Materials, copyright ©  2025
    American Chemical Society after peer review and technical editing by the publisher.
    To access the final edited and published work see https://doi.org/10.1021/acsaem.5c02113.
  identifier: http://rightsstatements.org/vocab/InC/1.0/

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo

start_date: 2025-09-29
end_date: 2026-09-29

## Journal

- title: ACS Applied Energy Materials
  issn: '25740962'
  volume: '8'
  issue: '19'
  start_page: 14052
  end_page: 14057

## Conference



## Related item



## Funding

- identifier: JPNP20003
  funder_name: New Energy and Industrial Technology Development Organization
- identifier: JPMXP1222HK0033
  funder_name: Ministry of Education, Culture, Sports, Science and Technology
- identifier: JPMXP1223HK0012
  funder_name: Ministry of Education, Culture, Sports, Science and Technology
- identifier: JPMXP1224HK0036
  funder_name: Ministry of Education, Culture, Sports, Science and Technology
- identifier: 20F19756
  funder_name: Japan Society for the Promotion of Science
- identifier: 25K01612
  funder_name: Japan Society for the Promotion of Science

## Instrument



## Instrument operator



## Instrument managing organization



## Measurement method



## Specimen



## Chemical composition



## Structure for specimen



## Structural feature for specimen



## Specific property for specimen



## Process for specimen treatment



## Computational method



## Energy level/transition state



## Software



## Custom property



## Fileset

- id: d516d493-d6d7-4fd8-a180-238d69e7f220
  filename: Bayesian_HOR_Manuscript_ver49.docx
  content_type: application/vnd.openxmlformats-officedocument.wordprocessingml.document
  size: 22796323
  md5: 80f0ac0601db054be206b9def5f8894c

## Thumbnail

fileset_id: d516d493-d6d7-4fd8-a180-238d69e7f220
filename: Bayesian_HOR_Manuscript_ver49.docx