Machine-learning-assisted prediction of highly active and stable PrBaCo<sub>2</sub>O<sub>5+<i>δ</i></sub>-based oxygen electrodes for solid oxide cells

A new highly active and stable PBC-based material was screened by a machine learning technique. The ABE and η descriptors are identified as important factors for the activity and stability of the PBC-based electrode, respectively.

Authors

Hailei Zhao (ORCID: https://orcid.org/0000-0001-5564-3677)
Jiayue Liu (ORCID: https://orcid.org/0000-0003-0853-8158)
Yaozong Yang
Min Zhang (ORCID: https://orcid.org/0009-0008-3588-6477)
Xian-Yu Wang (ORCID: https://orcid.org/0000-0002-0376-6365)

Institutions

University of Science and Technology Beijing (CN)

Publication Details

Journal: Journal of Materials Chemistry A
Published: 2025-01-01
DOI: https://doi.org/10.1039/d5ta05732a
Primary Topic: Advancements in Solid Oxide Fuel Cells
Type: article
Field-Weighted Citation Impact: 0.00

Funders

National Natural Science Foundation of China
National Key Research and Development Program of China

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article

Machine-learning-assisted prediction of highly active and stable PrBaCo₂O_5+δ-based oxygen electrodes for solid oxide cells

Hailei Zhao, Jiayue Liu, Yaozong Yang, Min Zhang et al.

Jan 1, 2025

Journal of Materials Chemistry A

Advancements in Solid Oxide Fuel Cells

article

Machine-learning-assisted prediction of highly active and stable PrBaCo₂O_5+δ-based oxygen electrodes for solid oxide cells

Hailei Zhao, Jiayue Liu, Yaozong Yang, Min Zhang, Xian-Yu Wang

January 1, 2025article en

Abstract

A new highly active and stable PBC-based material was screened by a machine learning technique. The ABE and η descriptors are identified as important factors for the activity and stability of the PBC-based electrode, respectively.

Journal of Materials Chemistry AVol. 13(37)

University of Science and Technology Beijing (CN)

National Natural Science Foundation of China, National Key Research and Development Program of China

Openalex Percentile: Top 83%

Advancements in Solid Oxide Fuel Cells

DOI Source OpenAlex

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