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Deciphering Key Descriptors for Scaling Relationships in Graphene-Supported Ptn Clusters via Machine Learning.

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  • 1Department of Chemistry, Indian Institute of Technology Indore, Indore, India.

Small (Weinheim an Der Bergstrasse, Germany)
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Summary
This summary is machine-generated.

Machine learning predicts the size-dependent activity of platinum subnano clusters for oxygen reduction reactions. This framework reveals insights into catalyst dynamics and guides the design of efficient electrocatalysts for fuel cells.

Keywords:
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Area of Science:

  • Materials Science
  • Electrochemistry
  • Computational Chemistry

Background:

  • Subnano clusters are promising electrocatalysts, but their complex behavior challenges activity evaluation.
  • Platinum (Pt) subnano clusters offer efficient noble metal utilization and high activity for electrochemical reactions.

Purpose of the Study:

  • To develop a machine learning (ML) framework for assessing size-dependent electrocatalytic activity.
  • To investigate the oxygen reduction reaction (ORR) using graphene-supported Ptn (n = 7–13) subnano clusters.

Main Methods:

  • Utilized non-ab initio descriptors to train an ML model.
  • Performed feature-driven uncertainty quantification to analyze catalytic behavior.
  • Derived reaction networks to understand rate-determining steps and binding site saturation.

Main Results:

  • The ML model accurately predicts adsorption energies of ORR intermediates.
  • Identified key geometric descriptors that break scaling relationships in subnanometer clusters.
  • Uncovered catalyst dynamics and structural evolution on supports under ORR conditions.

Conclusions:

  • Provided atomistic understanding of fluxional dynamics and structural evolution of subnano clusters on supports.
  • Offered guidelines for rational design of efficient electrocatalysts for fuel cells.
  • Highlighted the importance of ensemble representation and irregular catalytic activity in subnanometer regimes.