Symmetry-aware recursive image similarity exploration for materials microscopy

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Chemical Phenomena of Atomic Force Microscopy Scanning

Бекенстейн Ехонадав

PDF) Ferroelectric control of a Mott insulator

Figure S2: Color maps of time and energy dependence of reflectivity for

Machine Learning Pipeline for Segmentation and Defect Identification from High-Resolution Transmission Electron Microscopy Data, Microscopy and Microanalysis

Applied Sciences, Free Full-Text

Бекенстейн Ехонадав

VGG-Consulting (V. G. Gueorguiev, Ph.D.) on LinkedIn: A team developed an artificial neural network and used machine learning to…

Chemical Phenomena of Atomic Force Microscopy Scanning

Machine learning-based prediction and inverse design of 2D metamaterial structures with tunable deformation-dependent Poisson's ratio - Nanoscale (RSC Publishing) DOI:10.1039/D2NR02509D

Novel Machine Learning Technique To Identify Structural Similarities and Trends in Materials

PDF] Microscopy is All You Need

Reconstruction of Polarization Vortices by Diffraction Mapping of Ferroelectric PbTiO3 / SrTiO3 Superlattice Using a High Dynamic Range Pixelated Detector, Microscopy and Microanalysis