ArticleScience advances2022
Learning motifs and their hierarchies in atomic resolution microscopy.
Article in Science advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Atomic Defects in Layered Transition Metal Dichalcogenides for Sustainable Energy Storage and the Intelligent Trends in Data Analytics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Data-Driven Polyoxometalate Chemistry.Chemistry (Weinheim an der Bergstrasse, Germany) · 2025Article
- Deep learning for sub-ångström-resolution imaging in uncorrected scanning transmission electron microscopy.National science review · 2025Article
- Deep Learning Analysis of Localized Interlayer Stacking Displacement and Dynamics in Bilayer Phosphorene.Advanced materials (Deerfield Beach, Fla.) · 2025Article
- Auto-resolving the atomic structure at van der Waals interfaces using a generative model.Nature communications · 2025Article
- Deep-Learning-Based Mixture Identification for Nuclear Magnetic Resonance Spectroscopy Applied to Plant Flavors.Molecules (Basel, Switzerland) · 2023Article
- A multiscale generative model to understand disorder in domain boundaries.Science advances · 2023Article
- Bimetallic Sites for Catalysis: From Binuclear Metal Sites to Bimetallic Nanoclusters and Nanoparticles.Chemical reviews · 2023Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
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 MoS
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Registered trials
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