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Related Experiment Video

Updated: Jul 19, 2025

Picometer-Precision Atomic Position Tracking through Electron Microscopy

Picometer-Precision Atomic Position Tracking through Electron Microscopy

Published on: July 3, 2021

Quantifying Atomically Dispersed Catalysts Using Deep Learning Assisted Microscopy.

Haoyang Ni^1,2, Zhenyao Wu³, Xinyi Wu³

¹Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States.

|August 11, 2023

Summary

This summary is machine-generated.

A new convolutional neural network (CNN) algorithm automates the analysis of atomically dispersed catalysts (ADCs) using scanning transmission electron microscopy (STEM). This method accurately quantifies atomic configurations, overcoming limitations in large dataset analysis for catalyst research.

Keywords:

STEM catalyst convolutional neural network deep learning image analysis

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Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

Area of Science:

Materials Science
Catalysis
Data Science

Background:

Catalytic performance of atomically dispersed catalysts (ADCs) depends heavily on atomic arrangement.
Scanning transmission electron microscopy (STEM) images ADCs at the atomic scale.
Manual analysis of large STEM datasets for ADCs is time-consuming and challenging.

Purpose of the Study:

To develop an automated method for quantifying atomic configurations in ADCs.
To overcome the bottleneck of manual data analysis in STEM imaging of ADCs.
To enable efficient analysis of large-scale STEM datasets for catalyst research.

Main Methods:

Development of a convolutional neural network (CNN)-based algorithm.
Application of the algorithm to quantify spatial arrangements of adatom configurations.
Testing the algorithm on ADCs with varying support crystallinity and homogeneity.

Main Results:

The CNN algorithm accurately identifies atom positions.
The algorithm effectively analyzes large datasets of ADCs.
Demonstrated robustness across different support types and homogeneity levels.

Conclusions:

The developed CNN algorithm provides a robust solution for analyzing ADC atomic configurations from STEM data.
This method significantly accelerates the analysis of large datasets, addressing a key challenge in ADC research.
The algorithm shows potential for real-time analysis in future in situ microscopy experiments.