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January 1, 2025Journal of Materials Chemistry A

Machine learning-assisted computational screening of high-entropy alloy catalysts for HCOOH decomposition

Xin Liu Keju Sun Y.Z. Chen Xiufang Ma Changlin Yu Zhenzhou Su Hengfei He Jianlin Lu Hai‐Yan Su

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Key Points

Machine learning significantly speeds up the discovery of alloy catalysts for HCOOH decomposition.
The study integrates DFT calculations and microkinetic modeling to enhance catalyst efficiency.
This approach supports rapid advancements in material science and catalysis for energy conversion.
Findings may lead to more effective and sustainable catalytic processes in industrial applications.

Implication

Machine learning aids in discovering high-entropy alloy catalysts for HCOOH decomposition, suggesting faster advancements in catalytic efficiency.

Abstract

Machine-learning combined with DFT calculations and microkinetic modeling accelerates high-entropy alloy catalyst discovery for HCOOH decomposition.

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Cite This Study

Liu et al. (2025) studied this question.

synapsesocial.com/papers/68d4565431b076d99fa5b0a8 https://doi.org/10.1039/d5ta05112f

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