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Disentangling multiple scattering with deep learning:application to strain mapping from electron diffraction patterns

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npj Computational Materials 2022年第1期8卷 2419-2433页

作者： Joydeep Munshi Alexander Rakowski Benjamin H.Savitzky Steven E.Zeltmann jim ciston Matthew Henderson Shreyas Cholia Andrew M.Minor Maria K.Y.Chan Colin Ophus Center for Nanoscale Materials Argonne National LaboratoryArgonne DrWoodridge60517ILUSA National Center for Electron Microscopy Molecular FoundryLawrence Berkeley National Laboratory1 Cyclotron RoadBerkeley94720CAUSA Department of Materials Science and Engineering University of California BerkeleyBerkeley94720CAUSA Scientific Data Division Lawrence Berkeley National Laboratory1 Cyclotron RoadBerkeley94720CAUSA

A fast,robust pipeline for strain mapping of crystalline materials is important for many technological *** electron nanodiffraction allows us to calculate strain maps with high accuracy and spatial resolutions,but this technique is limited when the electron beam undergoes multiple ***-learning methods have the potential to invert these complex signals,but require a large number of training *** implement a Fourier space,complex-valued deep-neural network,FCU-Net,to invert highly nonlinear electron diffraction patterns into the corresponding quantitative structure factor ***-Net was trained using over 200,000 unique simulated dynamical diffraction patterns from different combinations of crystal structures,orientations,thicknesses,and microscope parameters,which are augmented with experimental *** evaluated FCU-Net against simulated and experimental datasets,where it substantially outperforms conventional analysis *** code,models,and training library are open-source and may be adapted to different diffraction measurement problems.

关键词： valued adapted crystalline

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In-situ atomic-scale observations of the oxidation of metals

In-situ atomic-scale observations of the oxidation of metals

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第十三届全国物理力学学术会议

作者： Guangwen Zhou Langli Luo Liang Li jim ciston Eric A.Stach Judith C Yang Department of Mechanical Engineering & Multidisciplinary Program in Materials Science and Engineering State University of New York at Binghamton National Center for Electron Microscopy Lawrence Berkeley National Laboratory Center for functional Nanomaterials Brookhaven National Laboratory Department of Chemical and Petroleum Engineering University of Pittsburgh

The canonical description of oxide formation in metals involves a solid-solid transformation proceeding with initial oxygen chemisorption induced reconstructions followed by oxygen subsurface *** a mechanism has been inferred from idealized experiments that are primarily restricted to planar surfaces under ultrahigh vacuum *** practice,however,metallic surfaces are seldom ***,they contain a high density of low-coordinated surface ***,in

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