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

Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences 2026 Open access

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Authors: Beatriz Andrea Choi Tan, N. A. Sofi, Thanh Ngoc Pham, Kurt Irvin M. Rojas, Samuel Eka Putra Payong Masan, Yuji Hamamoto, Ikutaro Hamada, Yoshitada Morikawa
DOI: 10.1098/rsta.2024.0555
Cited by: 1
References: 44

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'.

Fields of Study & Subjects

Electrocatalysts for Energy Conversion Machine Learning in Materials Science Catalysis and Oxidation Reactions

Publication details

Journal / source
Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences
Publisher
The Royal Society
Volume
384
Issue
2318
Pages
20240555
Publication date
16 April 2026
Language
EN