Rational ensemble design of alloy catalysts for selective ammonia oxidation based on machine learning

Yang, Jixing; Wang, Zhaojie; Liu, Zhang; Wang, Qingbo; Wen, Yanwei; Zhang, Aimin; Chen, Rong; Shan, Bin

doi:10.1039/d2ta07216e

J. Mater. Chem. A

2022

DOI: 10.1039/d2ta07216e

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Rational ensemble design of alloy catalysts for selective ammonia oxidation based on machine learning

Jixing Yang

Zhaojie Wang

Zhang Liu

et al.

Abstract: High throughput computation and machine learning studies are conducted for the rational design of ensembles on alloy catalysts for selective catalytic oxidation of NH3 (NH3-SCO). High throughput calculation is firstly...

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Cited by 6 publications

References 44 publications

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From Single Metals to High‐Entropy Alloys: How Machine Learning Accelerates the Development of Metal Electrocatalysts

Fan,

Chen,

Huang

et al. 2024

Adv Funct Materials

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The rapid advancement of high‐performance computing and artificial intelligence technology has opened up novel avenues for the development of various metal electrocatalysts. In particular, dilute and high‐entropy alloys have garnered significant attention owing to their unique electronic and spatial structures, as well as their exceptional electrocatalytic performance. Commencing with the exploration of single‐atom alloy catalysts, the latest advancements in machine learning (ML) techniques are presented for the efficient screening of a broad spectrum of metal spaces. Subsequently, the review delves into the prevailing trend in alloy research, focusing specifically on rare‐metal alloy electrocatalysts, and offers an overview of the progress and outcomes achieved through the application of ML in these domains. Finally, high‐entropy alloys are highlighted as a promising category of electrocatalysts and underscore the importance and potential applications of ML in addressing complex and challenging research issues are underscored.

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From Single Metals to High‐Entropy Alloys: How Machine Learning Accelerates the Development of Metal Electrocatalysts

Fan,

Chen,

Huang

et al. 2024

Adv Funct Materials

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show abstract

Active learning driven discovery of novel alloyed catalysts for selective ammonia oxidation

Yang,

Wang,

Zhang

et al. 2024

Chemical Engineering Journal

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Toward Next-Generation Heterogeneous Catalysts: Empowering Surface Reactivity Prediction with Machine Learning

Liu,

Peng

2024

Engineering

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Rational ensemble design of alloy catalysts for selective ammonia oxidation based on machine learning

Abstract: High throughput computation and machine learning studies are conducted for the rational design of ensembles on alloy catalysts for selective catalytic oxidation of NH3 (NH3-SCO). High throughput calculation is firstly...

Cited by 6 publications

References 44 publications

From Single Metals to High‐Entropy Alloys: How Machine Learning Accelerates the Development of Metal Electrocatalysts

From Single Metals to High‐Entropy Alloys: How Machine Learning Accelerates the Development of Metal Electrocatalysts

Active learning driven discovery of novel alloyed catalysts for selective ammonia oxidation

Toward Next-Generation Heterogeneous Catalysts: Empowering Surface Reactivity Prediction with Machine Learning

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