Predicting reactive graphene edge-adsorbed Pt single-atom catalyst structures for oxygen reduction via machine learning-enhanced DFT calculations

Single-atom platinum on graphitic materials can enhance catalysis of the oxygen reduction reaction (ORR). The local atomic structure shapes catalytic activity, but is not yet sufficiently understood due to its complexity. In this study, a machine learning-enhanced global structure search algorithm was combined with density functional theory calculations to explore previously unknown configurations of single-atom platinum adsorbed at graphene edges. Here, some of the novel stable structures demonstrated superior ORR catalytic performance compared to the conventional Pt(111) surface. This article is part of the theme issue 'Surfaces, interfaces and heterogeneous catalysis'.

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Publication Details

Journal
Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences
Published
2026-04-16
DOI
https://doi.org/10.1098/rsta.2024.0555
Citations
1
Primary Topic
Electrocatalysts for Energy Conversion
Type
article
Field-Weighted Citation Impact
4.41

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article

Predicting reactive graphene edge-adsorbed Pt single-atom catalyst structures for oxygen reduction via machine learning-enhanced DFT calculations

Yuji Hamamoto, Ikutaro Hamada, Yoshitada Morikawa, Kurt Irvin M. Rojas et al.
1 citations
Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences
Electrocatalysts for Energy Conversion
4.41
article

Predicting reactive graphene edge-adsorbed Pt single-atom catalyst structures for oxygen reduction via machine learning-enhanced DFT calculations

Yuji Hamamoto, Ikutaro Hamada, Yoshitada Morikawa, Kurt Irvin M. Rojas, Samuel Eka Putra Payong Masan, Beatriz Andrea Choi Tan, N. A. Sofi, Thanh Ngoc Pham
article en
1 citations

Abstract

Single-atom platinum on graphitic materials can enhance catalysis of the oxygen reduction reaction (ORR). The local atomic structure shapes catalytic activity, but is not yet sufficiently understood due to its complexity. In this study, a machine learning-enhanced global structure search algorithm was combined with density functional theory calculations to explore previously unknown configurations of single-atom platinum adsorbed at graphene edges. Here, some of the novel stable structures demonstrated superior ORR catalytic performance compared to the conventional Pt(111) surface. This article is part of the theme issue 'Surfaces, interfaces and heterogeneous catalysis'.

Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering SciencesVol. 384(2318)
Vietnam National University Ho Chi Minh City (VN), Osaka University of Economics (JP), Vietnam National University, Hanoi (VN), Okayama Prefectural University (JP), An Giang University (VN), The University of Osaka (JP)
Japan Science and Technology Agency
Openalex Percentile: Top 4%
Electrocatalysts for Energy Conversion
4.41
DOIOpen Access PDFOpenAlex
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Predicting reactive graphene edge-adsorbed Pt single-atom catalyst structures for oxygen reduction via machine learning-enhanced DFT calculations — Yuji Hamamoto, Ikutaro Hamada, et al. · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences (2026) | TGRS Research Map | TGRS