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The search for efficient fuel cell additives is a critical challenge for improving their oxygen reduction reaction activity. Here, we present the first successful application of active learning (AL) to guide the experimental discovery of organic molecules that enhance the mass activity of Pt cathode catalysts in polymer electrolyte fuel cells (PEFCs), a class of fuel cells often referred to in the literature as proton-exchange membrane fuel cells (PEMFCs). Using our in-house SMILES-X molecular characterization tool, we trained neural networks, autonomously tailored for our data, on an initial set of 96 organic compounds, of which 84 passed RDKit validity checks for modelling. Model performance improved over three AL cycles, with the mean absolute error decreasing from 0.202 to 0.165 and the root mean square error decreasing from 0.271 to 0.218. The cycles were guided by predictive ranking, experimental feasibility constraints, and LSTM-based molecule generation. Among the additives identified, melam, a melamine dimer, achieved a 67% improvement in mass activity over the no-additive baseline. These results demonstrate the feasibility of AL in low-data regimes for PEFC additive discovery and highlight the importance of data consistency, model retraining, and close ML–experiment coordination.

権利情報:

• Creative Commons BY Attribution 4.0 International
  

キーワード: materials science, machine learning, chemoinformatics, polymer electrolyte fuel cell (PEFC), oxygen reduction reaction, active learning

刊行年月日: 2026-08-01

出版者: IOP Publishing

掲載誌:

• Machine Learning: Science and Technology (ISSN: 26322153) vol. 7 issue. 4 045006

研究助成金:

• New Energy and Industrial Technology Development Organization

原稿種別: 出版者版 (Version of record)

MDR DOI:

公開URL: https://doi.org/10.1088/2632-2153/ae7f7c

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更新時刻: 2026-08-26 16:18:22 +0900

MDRでの公開時刻: 2026-08-26 18:30:08 +0900