Journal
ANGEWANDTE CHEMIE-INTERNATIONAL EDITION
Volume 59, Issue 43, Pages 19175-19183Publisher
WILEY-V C H VERLAG GMBH
DOI: 10.1002/anie.202006928
Keywords
artificial neural network; machine learning; nonprecious metal electrocatalysts; oxygen reduction; proton-exchange membrane fuel cells
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Funding
- National KeyRD Plan of China [2017YFB0102803]
- National Natural Science Foundation of China [21802069, 21676135]
- Ministry of Education for Equipment Pre-research [6141A02022531]
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Traditionally, a larger number of experiments are needed to optimize the performance of the membrane electrode assembly (MEA) in proton-exchange membrane fuel cells (PEMFCs) since it involves complex electrochemical, thermodynamic, and hydrodynamic processes. Herein, we introduce artificial intelligence (AI)-aided models for the first time to determine key parameters for nonprecious metal electrocatalyst-based PEMFCs, thus avoiding unnecessary experiments during MEA development. Among 16 competing algorithms widely applied in the AI field, decision tree and XGBoost showed good accuracy (86.7 % and 91.4 %) in determining key factors for high-performance MEA. Artificial neural network (ANN) shows the best accuracy (R2=0.9621) in terms of predictions of the maximum power density and a decent reproducibility (R2>0.99) on unchartedI-Vpolarization curves with 26 input features. Hence, machine learning is shown to be an excellent method for improving the efficiency of MEA design and experiments.
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