@article{21aac81f5aac432fa9536822e00c2af3,
title = "Quantifying Atomically Dispersed Catalysts Using Deep Learning Assisted Microscopy",
abstract = "The catalytic performance of atomically dispersed catalysts (ADCs) is greatly influenced by their atomic configurations, such as atom-atom distances, clustering of atoms into dimers and trimers, and their distributions. Scanning transmission electron microscopy (STEM) is a powerful technique for imaging ADCs at the atomic scale; however, most STEM analyses of ADCs thus far have relied on human labeling, making it difficult to analyze large data sets. Here, we introduce a convolutional neural network (CNN)-based algorithm capable of quantifying the spatial arrangement of different adatom configurations. The algorithm was tested on different ADCs with varying support crystallinity and homogeneity. Results show that our algorithm can accurately identify atom positions and effectively analyze large data sets. This work provides a robust method to overcome a major bottleneck in STEM analysis for ADC catalyst research. We highlight the potential of this method to serve as an on-the-fly analysis tool for catalysts in future in situ microscopy experiments.",
keywords = "STEM, catalyst, convolutional neural network, deep learning, image analysis",
author = "Haoyang Ni and Zhenyao Wu and Xinyi Wu and Smith, {Jacob G.} and Zachman, {Michael J.} and Zuo, {Jian Min} and Lili Ju and Guannan Zhang and Miaofang Chi",
note = "This research was conducted as part of an Early Career Award supported by the U.S. Department of Energy, Office of Basic Energy Sciences (DOE-BES), and by the DOE-BES, Division of Materials Sciences and Engineering. Algorithm developments were supported by the DOE Advanced Scientific Computing Research, Applied Mathematics Program. Microscopy experiments were performed at the Center for Nanophase Materials Sciences, a U.S. DOE Office of Science User Facility at Oak Ridge National Laboratory. This research was conducted as part of an Early Career Award supported by the U.S. Department of Energy, Office of Basic Energy Sciences (DOE-BES), and by the DOE-BES, Division of Materials Sciences and Engineering. Algorithm developments were supported by the DOE Advanced Scientific Computing Research, Applied Mathematics Program. Microscopy experiments were performed at the Center for Nanophase Materials Sciences, a U.S. DOE Office of Science User Facility at Oak Ridge National Laboratory. This manuscript has been authored by UT-Battelle, LLC under Contract DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan. This manuscript has been authored by UT-Battelle, LLC under Contract DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe-public-access-plan ).",
year = "2023",
month = aug,
day = "23",
doi = "10.1021/acs.nanolett.3c01892",
language = "English (US)",
volume = "23",
pages = "7442--7448",
journal = "Nano letters",
issn = "1530-6984",
publisher = "American Chemical Society",
number = "16",
}