Machine-learning assisted screening MXene-supported single-atom catalysts for oxygen reduction

Yan Zhang; Changwei Hu; Dianyong Tang; Zhishan Su

doi:10.1039/d5ta04542h

January 1, 2025Journal of Materials Chemistry A

Machine-learning assisted screening MXene-supported single-atom catalysts for oxygen reduction

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Key Points

Machine-learning screening improves the efficiency of catalysts for the oxygen reduction reaction (ORR), enhancing performance in fuel cells.
Key evidence indicates that utilizing MXene-supported single-atom catalysts can lead to significant advancements in ORR efficacy.
Assessment involves machine-learning techniques to identify optimal configurations for multifunctional electrocatalysts.
Implications point to a crucial direction for future research on clean energy technologies like proton-exchange membrane fuel cells.

Implication

Machine-learning assists in screening electrocatalysts for oxygen reduction in fuel cells, suggesting enhanced performance.

Abstract

The strategic design of cost-effective and high-performance multifunctional electrocatalysts for the oxygen reduction reaction (ORR) is of great importance, as ORR is a crucial half-reaction in proton-exchange membrane fuel cells...

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Cite This Study

Zhang et al. (2025) studied this question.

synapsesocial.com/papers/68af63e3ad7bf08b1eae423e https://doi.org/10.1039/d5ta04542h

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Machine-learning assisted screening MXene-supported single-atom catalysts for oxygen reduction

Key Points

Implication

Abstract

Discussion

Cite This Study

Machine-learning assisted screening MXene-supported single-atom catalysts for oxygen reduction

Key Points

Implication

Abstract

Discussion

Cite This Study

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