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Artificial-intelligence-assisted design principle for developing high-performance single-atom catalysts.

Liangliang Xu1, Xingkun Wang2,3, Xiaojuan Hu4

  • 1Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-Ro, Yuseong-Gu, Daejeon 34141, Republic of Korea.

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Summary
This summary is machine-generated.

Artificial intelligence (AI) combined with machine learning (ML) and data mining (DM) accelerates catalyst discovery. This AI strategy enhances transparency and reliability in developing high-performance catalysts for complex reactions.

Keywords:
artificial intelligencedata miningmachine learningoxygen reduction reactionsingle-atom catalysts

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Area of Science:

  • Materials Science
  • Catalysis
  • Computational Chemistry

Background:

  • Artificial intelligence (AI)-assisted approaches accelerate novel catalyst development.
  • Lack of mechanistic understanding in AI approaches can lead to unreliable results.
  • Elucidating underlying mechanisms is crucial for transparent and dependable AI-driven discoveries.

Purpose of the Study:

  • To develop an AI strategy combining machine learning (ML) and data mining (DM) for identifying high-performance catalysts.
  • To elucidate key factors governing catalytic performance in complex reactions.
  • To enhance the transparency and reliability of AI-assisted catalyst design.

Main Methods:

  • An AI strategy integrating ML and DM was developed.
  • The strategy was applied to evaluate 10,179 single-atom catalysts (SACs) for electrocatalytic oxygen reduction.
  • Experimental validation was performed to confirm the AI strategy's effectiveness.

Main Results:

  • Several high-performance single-atom catalysts (SACs) were identified for oxygen reduction.
  • Critical factors influencing SAC activity were determined.
  • The optimal Co-S2N2/g-SAC achieved a high half-wave potential of 0.92 V.

Conclusions:

  • The combined ML and DM AI strategy effectively identifies high-performance catalysts and elucidates performance-governing factors.
  • This approach enhances transparency and reliability in data-driven catalyst discovery.
  • The findings provide valuable insights for the rational design of advanced materials.