1. Towards automating structural discovery in scanning transmission electron microscopy* This manuscript has been authored by UT-Battelle, LLC, under Contract No. DE- AC0500OR22725 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 non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for the 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). Issue 1 (1st March 2022) Authors: Creange, Nicole; Dyck, Ondrej; Vasudevan, Rama K; Ziatdinov, Maxim; Kalinin, Sergei V Journal: Machine learning: science and technology Issue: Volume 3:Issue 1(2022) Page Start: Record Type: Journal Article View Content: Available online (eLD content is only available in our Reading Rooms) ↗