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Exploring Motifs and Their Hierarchies in Crystals via Unsupervised Learning

Published online by Cambridge University Press:  22 July 2022

Jiadong Dan
Affiliation:
NUS Centre for Bioimaging Sciences, National University of Singapore, Singapore Dept. of Biological Science, National University of Singapore, Singapore
Xiaoxu Zhao
Affiliation:
School of Materials Science and Engineering, Nanyang Technological University, Singapore
Qian He
Affiliation:
Dept. of Materials Science and Engineering, National University of Singapore, Singapore
N. Duane Loh*
Affiliation:
NUS Centre for Bioimaging Sciences, National University of Singapore, Singapore Dept. of Biological Science, National University of Singapore, Singapore Dept. of Physics, National University of Singapore, Singapore
Stephen J. Pennycook*
Affiliation:
Dept. of Materials Science and Engineering, University of Tennessee, Knoxville, TN, USA School of Physical Sciences, University of Chinese Academy of Sciences, Beijing, China
*
*Corresponding author: [email protected] (N.D.L); [email protected] (S.J.P).
*Corresponding author: [email protected] (N.D.L); [email protected] (S.J.P).

Abstract

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Type
Artificial Intelligence, Instrument Automation, And High-dimensional Data Analytics for Microscopy and Microanalysis
Copyright
Copyright © Microscopy Society of America 2022

References

Granda, JM et al. , Nature 559 (2018), doi:10.1038/s41586-018-0307-8CrossRefGoogle Scholar
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S. J. P. acknowledges funding from Singapore Ministry of Education Tier 1 grant R-284-000-172-114, Tier 2 grant R-284-000-175-112. N.D.L acknowledges funding support from the National Research Foundation (grant number NRF-CRP16-2015-05), and the NUS Early Career award (A-0004744-00-00).Google Scholar