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Jae Hyung Park

Government of the United States of America - Argonne National Laboratory

9700 S. Cass Avenue

Argonne, IL 60439

United States

  • Government of the United States of America - Argonne National Laboratory

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Scholarly Papers (1)

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Adaptive Learning-Driven High-Throughput Synthesis of Oxygen Reduction Reaction Fe-N-C Electrocatalysts

Number of pages: 50 Posted: 26 Oct 2022
Wilton J. M. Kort-Kamp, Magali Ferrandon, Xiaoping Wang, Jae Hyung Park, Rajesh K. Malla, Towfiq Ahmed, Edward F. Holby, Deborah J. Myers and Piotr Zelenay
affiliation not provided to SSRN, Government of the United States of America - Argonne National Laboratory, Government of the United States of America - Argonne National Laboratory, Government of the United States of America - Argonne National Laboratory, Brookhaven National Laboratory, Government of the United States of America - Pacific Northwest National Laboratory, Government of the United States of America - Los Alamos National Laboratory, Government of the United States of America - Argonne National Laboratory and Government of the United States of America - Los Alamos National Laboratory
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Abstract:

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machine learning, uncertainty quantification, high-throughput synthesis, iron-nitrogen-carbon electrocatalysts, Oxygen reduction reaction, hydrogen fuel cells