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Screening binary ORR catalysts with physics-informed machine learning by incorporating conditions and ensembles

AI editorPublished 2026.09.09

An ACS Catalysis paper screened binary ORR catalysts using physics-informed machine learning that incorporates reaction condition and ensemble information. Additional DFT verification confirmed the candidate Co/Cr, Co/Ag, Co/Ru, Co/Ir, and Co/Zn, and the study reported consistency with experimental trends. [1]

1/ AI utilization method: physics-informed machine learning
2/ Applied task: screening binary ORR catalysts by reflecting reaction conditions and ensemble information
3/ Reported result: candidates Co/Cr, Co/Ag, Co/Ru, Co/Ir, and Co/Zn were confirmed by DFT, and consistency with experimental trends was reported
4/ The paper published in ACS Catalysis used physics-informed machine learning that reflects both reaction condition and ensemble information to perform screening of binary ORR catalysts.
5/ Through additional DFT verification, candidates such as Co/Cr, Co/Ag, Co/Ru, Co/Ir, and Co/Zn were confirmed, and consistency with experimental trends was also reported.
6/ For materials researchers, the significance of this work lies in the attempt to improve screening accuracy by including reaction conditions and surface ensemble effects in the model, rather than selecting catalyst candidates based only on composition.
7/ In particular, for problems such as ORR, where surface state and reaction environment have a large influence, using ML with physical constraints can increase the interpretability of candidate ranking.
8/ However, although DFT verification and consistency with experimental trends were reported, there is no guarantee that this approach will work equally well for other transition-metal combinations or other electrochemical reactions.
9/ If the condition definitions change, the input features and physical constraints must also be adjusted again, and model predictions should be revalidated through independent calculations and experiments. [1]

How to apply this

Try screening catalyst candidates with physics-informed ML that incorporates both reaction conditions and ensemble effects.

  1. Set the target ORR performance and reaction-condition variables, and collect structure, composition, and ensemble information in the same format.
  2. Train a machine-learning model with physical constraints to rank candidates, and compare predictions across conditions.
  3. Revalidate the top candidates with DFT and, if possible, compare them with experimental data to confirm whether the predictions and trends agree.
Data needed
Catalyst data that include structure, composition, surface ensemble, reaction conditions, and reference calculated or measured values are needed.
Checking the result
Confirm the top candidates with separate DFT calculations and, if possible, independent experiments, and check whether the ranking is maintained when conditions change.
What can go wrong
If reaction condition information is incomplete, the model may learn spurious correlations instead of surface-state effects, so condition variables must be organized consistently.

이 가이드는 원문의 방법을 바탕으로 AI가 제안한 적용 방향입니다. 원문에 적힌 내용이 아니며 같은 결과가 재현된다는 보장도 아닙니다.

Related concepts and practice

이 이슈를 읽는 데 필요한 개념입니다. 용어마다 Colab에서 바로 돌려볼 수 있는 실습 노트북이 있습니다.

Sources

  1. Reaction-Condition- and Ensemble-Aware Screening of Dual-Atom ORR Catalysts via Physics-Informed Machine Learning | ACS Catalysis | ACS Publications

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