Design of Multifunctional Catalysts and Potential-Dependent Inverted Adsorption: A Sweet Marriage of Synergistic “Genome” Investigated by Machine Learning and First-Principles Calculations

The pursuit of two-dimensional single-atom catalysts (SACs) holds profound importance in the quest for efficient, stable, and economical alternatives to noble metals in hydrogen evolution (HER), oxygen evolution (OER), and oxygen reduction (ORR) reactions. By harnessing the remarkable tunability of transition metal (TM) active centers and their coordination environments coupled with the diversity of substrate materials, these catalysts present unprecedented opportunities for the design of highly stable and active electrocatalysts. This study offers a comprehensive exploration of the catalytic HER/OER/ORR activity in 207 ((TM-NxHxO4–x)3@g-C12B12N12H12) SACs, utilizing a seamless integration of density functional theory (DFT) and machine learning (ML). Twelve bifunctional OER/ORR catalysts with overpotentials exceeding Pt/IrO2 were identified, with (Rh-N2H2O2-V)3@g-C12B12N12H12 and (Rh-N1H1O3)3@g-C12B12N12H12 monolayers emerging as standout trifunctional electrocatalysts due to their remarkably low HER overpotential. Through the Sure Independence Screening and Sparsifying Operator (SISSO) method, a clear relationship between intrinsic properties and activity was uncovered, with its acceptable prediction accuracy in intermediates adsorption affirmed. The constant-potential method further underscores the essential role of electric double-layer capacitance in modulating the kinetic barrier of the rate-determining step in them. We observed shifts in the Fermi level with changing electrochemical potential altering d-orbital occupation, highlighting the synergy between metal atomic orbitals and the band of substrate. With O2 as the model adsorbate, we reveal the direct impact of these shifts on adsorption energies, uncovering a fascinating inversion of adsorption energies on Rh under varying electrochemical potentials. Such findings are driven by the filling of the dxz orbital, which plays a pivotal role in stabilizing the O2 π orbital and reshaping the electronic structure.

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Publication Details

Journal
ACS Sustainable Chemistry & Engineering
Published
2025-06-13
DOI
https://doi.org/10.1021/acssuschemeng.5c02002
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00

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article

Design of Multifunctional Catalysts and Potential-Dependent Inverted Adsorption: A Sweet Marriage of Synergistic “Genome” Investigated by Machine Learning and First-Principles Calculations

Weiju Hao, Yali Lu, Qiang Zhang, Yu-Ling Song et al.
3 links
ACS Sustainable Chemistry & Engineering
Machine Learning in Materials Science
article

Design of Multifunctional Catalysts and Potential-Dependent Inverted Adsorption: A Sweet Marriage of Synergistic “Genome” Investigated by Machine Learning and First-Principles Calculations

Weiju Hao, Yali Lu, Qiang Zhang, Yu-Ling Song, Yuanrui Li, Zongjin Hu
article en

Abstract

The pursuit of two-dimensional single-atom catalysts (SACs) holds profound importance in the quest for efficient, stable, and economical alternatives to noble metals in hydrogen evolution (HER), oxygen evolution (OER), and oxygen reduction (ORR) reactions. By harnessing the remarkable tunability of transition metal (TM) active centers and their coordination environments coupled with the diversity of substrate materials, these catalysts present unprecedented opportunities for the design of highly stable and active electrocatalysts. This study offers a comprehensive exploration of the catalytic HER/OER/ORR activity in 207 ((TM-NxHxO4–x)3@g-C12B12N12H12) SACs, utilizing a seamless integration of density functional theory (DFT) and machine learning (ML). Twelve bifunctional OER/ORR catalysts with overpotentials exceeding Pt/IrO2 were identified, with (Rh-N2H2O2-V)3@g-C12B12N12H12 and (Rh-N1H1O3)3@g-C12B12N12H12 monolayers emerging as standout trifunctional electrocatalysts due to their remarkably low HER overpotential. Through the Sure Independence Screening and Sparsifying Operator (SISSO) method, a clear relationship between intrinsic properties and activity was uncovered, with its acceptable prediction accuracy in intermediates adsorption affirmed. The constant-potential method further underscores the essential role of electric double-layer capacitance in modulating the kinetic barrier of the rate-determining step in them. We observed shifts in the Fermi level with changing electrochemical potential altering d-orbital occupation, highlighting the synergy between metal atomic orbitals and the band of substrate. With O2 as the model adsorbate, we reveal the direct impact of these shifts on adsorption energies, uncovering a fascinating inversion of adsorption energies on Rh under varying electrochemical potentials. Such findings are driven by the filling of the dxz orbital, which plays a pivotal role in stabilizing the O2 π orbital and reshaping the electronic structure.

ACS Sustainable Chemistry & EngineeringVol. 13(25)
University of Shanghai for Science and Technology (CN), Nanyang Normal University (CN)
Natural Science Foundation of Shanghai, National Natural Science Foundation of China
Zero hunger
Openalex Percentile: Top 91%
Machine Learning in Materials Science
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