Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

AtomVision: A machine vision library for atomistic images

Published

Author(s)

Brian DeCost, Ramya Gurunathan, Adam Biacchi, Kamal Choudhary

Abstract

Computer vision techniques have immense potential for materials design applications. In this work, we introduce an integrated and general-purpose AtomVision library that can be used to generate and curate microscopy image (such as scanning tunneling microscopy and scanning transmission electron microscopy) data sets and apply a variety of machine learning techniques. To demonstrate the applicability of this library, we (1) establish an atomistic image data set of about 10 000 materials with large structural and chemical diversity, (2) develop and compare convolutional and atomistic line graph neural network models to classify the Bravais lattices, (3) demonstrate the application of fully convolutional neural networks using U-Net architecture to pixelwise classify atom versus background, (4) use a generative adversarial network for super resolution, (5) curate an image data set on the basis of natural language processing using an open-access arXiv data set, and (6) integrate the computational framework with experimental microscopy images for Rh, Fe3O4, and SnS systems. The AtomVision library is available at https://github.com/usnistgov/atomvision.
Citation
Journal of Chemical Information and Modeling

Citation

DeCost, B. , Gurunathan, R. , Biacchi, A. and Choudhary, K. (2023), AtomVision: A machine vision library for atomistic images, Journal of Chemical Information and Modeling, [online], https://doi.org/10.1021/acs.jcim.2c01533, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=935950 (Accessed June 22, 2024)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created March 1, 2023, Updated March 29, 2023