- Yang, Yao;
- Zhou, Jihan;
- Zhao, Zipeng;
- Sun, Geng;
- Moniri, Saman;
- Ophus, Colin;
- Yang, Yongsoo;
- Wei, Ziyang;
- Yuan, Yakun;
- Zhu, Cheng;
- Sun, Qiang;
- Jia, Qingying;
- Heinz, Hendrik;
- Ciston, Jim;
- Ercius, Peter;
- Sautet, Philippe;
- Huang, Yu;
- Miao, Jianwei
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.