Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/164370
Title: Learning motifs and their hierarchies in atomic resolution microscopy
Authors: Dan, Jiadong
Zhao, Xiaoxu
Ning, Shoucong
Lu, Jiong
Loh, Kian Ping
He, Qian
Loh, N. Duane
Pennycook, Stephen J.
Keywords: Engineering::Materials
Issue Date: 2022
Source: Dan, J., Zhao, X., Ning, S., Lu, J., Loh, K. P., He, Q., Loh, N. D. & Pennycook, S. J. (2022). Learning motifs and their hierarchies in atomic resolution microscopy. Science Advances, 8(15), eabk1005-. https://dx.doi.org/10.1126/sciadv.abk1005
Project: 03INS000973C150 
Journal: Science Advances 
Abstract: Characterizing materials to atomic resolution and first-principles structure-property prediction are two pillars for accelerating functional materials discovery. However, we are still lacking a rapid, noise-robust framework to extract multilevel atomic structural motifs from complex materials to complement, inform, and guide our first-principles models. Here, we present a machine learning framework that rapidly extracts a hierarchy of complex structural motifs from atomically resolved images. We demonstrate how such motif hierarchies can rapidly reconstruct specimens with various defects. Abstracting complex specimens with simplified motifs enabled us to discover a previously unidentified structure in a Mo─V─Te─Nb polyoxometalate (POM) and quantify the relative disorder in a twisted bilayer MoS2. In addition, these motif hierarchies provide statistically grounded clues about the favored and frustrated pathways during self-assembly. The motifs and their hierarchies in our framework coarse-grain disorder in a manner that allows us to understand a much broader range of multiscale samples with functional imperfections and nontrivial topological phases.
URI: https://hdl.handle.net/10356/164370
ISSN: 2375-2548
DOI: 10.1126/sciadv.abk1005
Schools: School of Materials Science and Engineering 
Rights: © 2022 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MSE Journal Articles

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