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Mapping Distinct Oxygen-Binding Regimes in Single-Atom Alloys

  • Xiaojuan Hu
  • , Iftikhar Hussain*
  • , Zhong-Kang Han*
  • *Corresponding author for this work

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

Abstract

Single-atom alloys greatly expand the design space of heterogeneous catalysts, yet adsorbate binding on these materials often cannot be captured by a single universal principle. Here, we show that oxygen binding on single-atom alloys is instead governed by distinct adsorption regimes that arise from different physical origins. Using more than 1,000 density functional theory calculations, we combine interpretable machine learning with descriptor analysis to identify three governing mechanisms and define their domains of validity. Oxygen binding is controlled by the d-band center theory on early transition metal hosts, follows the ten-electron counting rule on late transition metal hosts, and is further modulated by coordination effects in a complementary structural regime. This regime-based framework explains why conventional global models fail to capture adsorption trends across chemically diverse alloys. By accounting for distinct physical regimes, it enables more reliable prediction of oxygen binding and supports high-throughput screening of single-atom alloys. Our results establish adsorption-regime mapping as a general strategy for interpretable catalyst discovery beyond single-descriptor approaches. © 2026 Wiley-VCH GmbH.
Original languageEnglish
Article numbere74709
Number of pages9
JournalSmall
Online published29 Jul 2026
DOIs
Publication statusOnline published - 29 Jul 2026

Funding

We acknowledge the financial support of the National Key Research and Development Program of China (2023YFA1506902, 2025YFA1016804), the National Natural Science Foundation of China (22302173), the Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang (2023R01007), the Zhejiang Provincial Natural Science Foundation of China under Grant No. LQK26B030001, and the Zhejiang Key Laboratory of Low-Carbon Synthesis of Value-Added Chemicals (2025E10097).

Research Keywords

  • adsorbate binding mechanisms
  • high-throughput screening
  • physics-informed machine learning
  • single-atom alloy catalysts
  • subgroup discovery algorithm

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