Unsupervised machine learning discovery of structural units and transformation pathways from imaging data

Unsupervised machine learning discovery of structural units and transformation pathways from imaging data

We show that unsupervised machine learning can be used to learn chemical transformation pathways from observational Scanning Transmission Electron Microscopy (STEM) data. To enable this analysis, we assumed the existence of atoms, a discreteness of atomic classes, and the presence of an explicit rel...

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Bibliographic Details
Main Authors:	Sergei V. Kalinin, Ondrej Dyck, Ayana Ghosh, Yongtao Liu, Bobby G. Sumpter, Maxim Ziatdinov
Format:	Article
Language:	English
Published:	AIP Publishing LLC 2023-06-01
Series:	APL Machine Learning
Online Access:	http://dx.doi.org/10.1063/5.0147316

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