Atomic-scale identification of the active sites of nanocatalysts

TL;DR

This paper combines advanced atomic electron tomography with machine learning to identify and understand the active sites of nanocatalysts at the atomic level, improving the ability to predict catalytic activity.

Contribution

It introduces a novel approach integrating 3D atomic structure determination with machine learning to pinpoint active catalytic sites in nanocatalysts.

Findings

Identified active sites in PtNi and Mo-doped PtNi nanocatalysts.

Established a structure-activity relationship descriptor.

Validated predictions with electrochemical measurements.

Abstract

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.