Big data and deep data in scanning and electron microscopies: deriving functionality from multidimensional data sets.
Alex Belianinov,Rama K. Vasudevan,Evgheni Strelcov,Chad A. Steed,Sang Mo Yang,Alexander Tselev,Stephen Jesse,Michael D. Biegalski,Galen M. Shipman,Christopher T. Symons,Albina Y. Borisevich,Rick Archibald,Sergei V. Kalinin +12 more
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TLDR
Here, several recent applications of the big and deep data analysis methods are reviewed to visualize, compress, and translate this multidimensional structural and functional data into physically and chemically relevant information.Abstract:
The development of electron and scanning probe microscopies in the second half of the twentieth century has produced spectacular images of the internal structure and composition of matter with nanometer, molecular, and atomic resolution. Largely, this progress was enabled by computer-assisted methods of microscope operation, data acquisition, and analysis. Advances in imaging technology in the beginning of the twenty-first century have opened the proverbial floodgates on the availability of high-veracity information on structure and functionality. From the hardware perspective, high-resolution imaging methods now routinely resolve atomic positions with approximately picometer precision, allowing for quantitative measurements of individual bond lengths and angles. Similarly, functional imaging often leads to multidimensional data sets containing partial or full information on properties of interest, acquired as a function of multiple parameters (time, temperature, or other external stimuli). Here, we review several recent applications of the big and deep data analysis methods to visualize, compress, and translate this multidimensional structural and functional data into physically and chemically relevant information.read more
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Research Update: Towards designed functionalities in oxide-based electronic materials
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Reducing Time to Discovery: Materials and Molecular Modeling, Imaging, Informatics, and Integration.
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TL;DR: The M3I3 initiative aims to reduce the time for the discovery, design and development of materials based on elucidating multiscale processing-structure-property relationship and materials hierarchy, which are to be quantified and understood through a combination of machine learning and scientific insights as discussed by the authors.
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Practical aspects of diffractive imaging using an atomic-scale coherent electron probe.
Zhen Chen,Matthew Weyland,Peter Ercius,Jim Ciston,Changxi Zheng,Michael S. Fuhrer,Adrian J. D’Alfonso,Leslie J. Allen,Scott D. Findlay +8 more
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Constraining Data Mining with Physical Models: Voltage- and Oxygen Pressure-Dependent Transport in Multiferroic Nanostructures.
TL;DR: Deep data analysis allows extraction of local dopant concentrations and barrier heights empowering the understanding of the underlying dynamic mechanisms of resistive switching, thus distilling physical meaning out of raw data.
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Quantitative Analysis of the Local Phase Transitions Induced by Laser Heating.
TL;DR: This work applied local heating coupled with piezoresponse force microscopy and confocal Raman spectroscopy for nanoscale investigations of a ferroelectric-paraelectric phase transition in the copper indium thiophosphate layered ferroElectric.
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