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Cited by 92Fuel Cells and Related MaterialsElectrocatalysts for Energy ConversionMachine Learning in Materials Science

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Designing AI‐Aided Analysis and Prediction Models for Nonprecious Metal Electrocatalyst‐Based Proton‐Exchange Membrane Fuel Cells

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Ding, Rui, Wang, Ran, Ding, Yiqin, Yin, Wenjuan, Liu, Yide, Li, Jia, Liu, Jianguo·2020·Angewandte Chemie International Edition
DOI 10.1002/anie.202006928
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Abstract

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 uncharted I – V polarization 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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Crossref · 10.1002/anie.202006928

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  • The artificial neural network (ANN) is the most accurate machine learning algorithm and has suggested that backpressure, cathodic loading amount and BET surface area are the most important parameters for PEM fuel cell operation and cathode catalysts.

    “The artificial neural network (ANN) is the most accurate machine learning algorithm and has suggested that the backpressure, cathodic loading amount and BET surface area are the most important parameters for PEM fuel cell operation and cathode catalysts (Fig. 3b).”

    From Recent Advances on PEM Fuel Cells: From Key Materials to Membrane Electrode Assembly (2023) · cites this paper as [57]

  • A reliable ensemble learner, XGBoost, was established and trained to rank the degrees of importance among 26 complicated features in PEM fuel cells.

    “As shown in Fig. 3a, b, a reliable ensemble learner, XGBoost, was established and trained to rank the degrees of importance among 26 complicated features in PEM fuel cells.”

    From Recent Advances on PEM Fuel Cells: From Key Materials to Membrane Electrode Assembly (2023) · cites this paper as [57]

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Metadata

Authors
Ding, R., Wang, R., Ding, Y., Yin, W., Liu, Y., Li, J., & Liu, J.
Affiliations
Collaborative Innovation Center of Advanced Microstructures (CN)
Corresponding author
  • Li, Jia
  • Liu, Jianguo
ORCID
Venue
Angewandte Chemie International Edition
Publisher
Wiley
Published
2020-08-24
Volume
59
Issue
43
Pages
19175-19183
ISSN
  • 1433-7851 (ISSN-L)
  • 1521-3773
DOI
10.1002/anie.202006928
OpenAlex
W3041568745
Cited by
92
References
4
Citations by year
2026: 12 · 2025: 19 · 2024: 12 · 2023: 20 · 2022: 12 · 2021: 16 · 2020: 1
FWCI
4.21
Type
journal article
Language
en
Open access
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations
  • Angewandte Chemie International Edition (publishedVersion)
  • PubMed (publishedVersion)
Funders
National Natural Science Foundation of China (2017YFB0102803, 21802069, 21676135)
Topics
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Fuel Cells and Related Materials
  • Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Electrocatalysts for Energy Conversion
  • Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
Keywords
proton exchange membrane fuel cells, non-precious metal electrocatalysts, membrane electrode assembly, machine learning, artificial neural networks, XGBoost, decision tree

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