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RESEARCH PAPER · InfoMat · 2019

A machine perspective of atomic defects in scanning transmission electron microscopy

Jiadong Dan, Xiaoxu Zhao, Stephen John Pennycook

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A machine perspective of atomic defects in scanning transmission electron microscopyOriginalWSe 2 2D p‐type semiconductor‐based electronic devices for information technology: Design, preparation, and applications259Origin of functionality for functional materials at atomic scale22Electron beam triggered single-atom dynamics in two-dimensional materials16Machine learning in materials design: Algorithm and application*62Unlocking the origin of compositional fluctuations in InGaN light emitting diodes16Synthesis of Wafer‐Scale Graphene with Chemical Vapor Deposition for Electronic Device Applications110Learning-based defect recognition for quasi-periodic HRSTEM images14Machine Learning Pipeline for Segmentation and Defect Identification from High-Resolution Transmission Electron Microscopy Data76Quantitative annular dark-field imaging in the scanning transmission electron microscope—a review12Differential programming enabled functional imaging with Lorentz transmission electron microscopy24Atomic-level defect modulation and characterization methods in 2D materials44Materials Informatics for Mechanical Deformation: A Review of Applications and Challenges67Atomic resolution imaging of light elements in low-dimensional materials0Compressed sensing for scanning tunnel microscopy imaging of defects and disorder4Applications of deep learning in electron microscopy72Laser Irradiation Induced Atomic Structure Modifications in Strontium Titanate20Tailoring atomic 1T phase CrTe 2 for in situ fabrication13Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook134Machine learning in nuclear materials research125An Automatic Magnetic Domain Characterization Method by Intelligent Algorithms assisted Kerr microscopy0Machine learning in scanning transmission electron microscopy177Machine learning in scanning transmission electron microscopy10Hybrid Deep Learning Crystallographic Mapping of Polymorphic Phases in Polycrystalline Hf0.5Zr0.5O2 Thin Films14Machine Learning–Assisted Thin-Film Transistor Characterization: A Case Study of Amorphous Indium Gallium Zinc Oxide (IGZO) Thin-Film Transistors6STEM Image Analysis Based on Deep Learning: Identification of Vacancy Defects and Polymorphs of MoS245Review in situ transmission electron microscope with machine learning33Emerging machine learning strategies for diminishing measurement uncertainty in SPM nanometrology5Image-based machine learning for materials science54Machine learning for materials classifications from images4Machine learning-augmented surface-enhanced spectroscopy toward next-generation molecular diagnostics141In situ electron microscopy: modalities of dynamic measurements to capture fundamental physical or chemical processes down to the atomic scale0nNPipe: a neural network pipeline for automated analysis of morphologically diverse catalyst systems27Neural network approach for ferroelectric hafnium oxide phase identification at the atomistic scale10Review: 2D material property characterizations by machine-learning-assisted microscopies16Methods, progresses, and opportunities of materials informatics72Advancing electron microscopy using deep learning15Quantitative gas-phase transmission electron microscopy: Where are we now and what comes next?16Simultaneous secondary electron microscopy in the scanning transmission electron microscope with applications for in situ studies12Structural degeneracy and formation of crystallographic domains in epitaxial LaFeO3 films revealed by machine-learning assisted 4D-STEM10Combining low-loss EELS experiments with machine learning-based algorithms to automate the phases separation imaging in industrial duplex stainless steels0Atomic Fabrication of 2D Materials Using Electron Beams Inside an Electron Microscope4Tutorial on In Situ and Operando (Scanning) Transmission Electron Microscopy for Analysis of Nanoscale Structure–Property Relationships24Artificial intelligence and deep learning in electron microscopy0Highly efficient room-temperature ethylene sensing with molybdenum based transition metal dichalcogenides14Automated atomic site determination by four-dimensional scanning transmission electron microscopy data analytics0Atomic Defects in Layered Transition Metal Dichalcogenides for Sustainable Energy Storage and the Intelligent Trends in Data Analytics4Ai-assisted atomic-scale analysis in STEM images0Automating Identification of Iodine Cluster Distortions in InSeI using Blob Detection0Artificial intelligence for carbon materials in electrochemical energy storage: Methods, applications, and perspectives0

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  23. Hybrid Deep Learning Crystallographic Mapping of Polymorphic Phases in Polycrystalline Hf0.5Zr0.5O2 Thin Films

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