Alloy nanocatalysts have found broad applications ranging from fuel cells to catalytic converters and hydrogenation reactions. Despite extensive studies, identifying the active sites of nanocatalysts remains a major challenge due to the heterogeneity of the local atomic environment. Here, we advance atomic electron tomography to determine the 3D local atomic structure, surface morphology and chemical composition of PtNi and Mo-doped PtNi nanocatalysts. Using machine learning trained by density functional theory calculations, we identify the catalytic active sites for the oxygen reduction reaction from experimental 3D atomic coordinates, which are corroborated by electrochemical measurements. By quantifying the structure-activity relationship, we discover a local environment descriptor to explain and predict the catalytic active sites at the atomic level. The ability to determine the 3D atomic structure and chemical species coupled with machine learning is expected to expand our fundamental understanding of a wide range of nanocatalysts.
@article{arxiv.2202.09460,
title = {Atomic-scale identification of the active sites of nanocatalysts},
author = {Yao Yang and Jihan Zhou and Zipeng Zhao and Geng Sun and Saman Moniri and Colin Ophus and Yongsoo Yang and Ziyang Wei and Yakun Yuan and Cheng Zhu and Qiang Sun and Qingying Jia and Hendrik Heinz and Jim Ciston and Peter Ercius and Philippe Sautet and Yu Huang and Jianwei Miao},
journal= {arXiv preprint arXiv:2202.09460},
year = {2022}
}