Machine-learning-assisted prediction of highly active and stable PrBaCo₂O5+δ-based oxygen electrodes for solid oxide cells

Min Zhang; Jiayue Liu; Yaozong Yang; Xian-Yu Wang; Zhihong Du; Hailei Zhao

doi:10.1039/d5ta05732a

January 1, 2025Journal of Materials Chemistry A

Machine-learning-assisted prediction of highly active and stable PrBaCo₂O5+δ-based oxygen electrodes for solid oxide cells

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Key Points

The study identifies key descriptors that affect the activity and stability of PrBaCo2O5+δ electrodes in solid oxide cells.
Machine learning techniques were employed to screen a new PBC-based material, revealing promising results for use in energy applications.
This research highlights the role of ABE and η descriptors as critical factors in determining electrode performance.
The findings support the development of advanced materials, suggesting machine learning can enhance solid oxide cell technology.

Implication

Machine learning predicts enhanced activity and stability in PrBaCo2O5+δ oxygen electrodes, indicating potential for solid oxide cells.

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.

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Cite This Study

Zhang et al. (2025) studied this question.

synapsesocial.com/papers/68bb3a492b87ece8dc95586b https://doi.org/10.1039/d5ta05732a

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Machine-learning-assisted prediction of highly active and stable PrBaCo₂O5+δ-based oxygen electrodes for solid oxide cells

Key Points

Implication

Abstract

Discussion

Cite This Study

Machine-learning-assisted prediction of highly active and stable PrBaCo₂O5+δ-based oxygen electrodes for solid oxide cells

Key Points

Implication

Abstract

Discussion

Cite This Study

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