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Symmetry-aware recursive image similarity exploration for materials microscopy

npj Computational Materials 2021年第1期7卷 1508-1521页

作者： Tri N.M.Nguyen Yichen Guo Shuyu Qin Kylie S.Frew Ruijuan Xu Joshua C.agar Department of Materials Science and Engineering Lehigh UniversityBethlehemPA 18015USA Department of Computer Science and Engineering Lehigh UniversityBethlehemPA 18015USA Department of Mechanical Engineering Lehigh UniversityBethlehemPA 18015USA Department of Applied Physics Stanford UniversityStanfordCA 94305USA

In pursuit of scientific discovery,vast collections of unstructured structural and functional images are acquired;however,only an infinitesimally small fraction of this data is rigorously analyzed,with an even smaller fraction ever being *** method to accelerate scientific discovery is to extract more insight from costly scientific experiments already ***,data from scientific experiments tend only to be accessible by the originator who knows the experiments and ***,there are no robust methods to search unstructured databases of images to deduce correlations and ***,we develop a machine learning approach to create image similarity projections to search unstructured image *** improve these projections,we develop and train a model to include symmetry-aware *** an exemplar,we use a set of 25,133 piezoresponse force microscopy images collected on diverse materials systems over five *** demonstrate how this tool can be used for interactive recursive image searching and exploration,highlighting structural similarities at various length *** tool justifies continued investment in federated scientific databases with standardized metadata schemas where the combination of filtering and recursive interactive searching can uncover synthesis-structure-property *** provide a customizable open-source package(https://***/m3-learning/Recursive_Symmetry_Aware_Materials_Microstructure_Explorer)of this interactive tool for researchers to use with their data.

关键词： interactive image similarity

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Application of a long short-term memory for deconvoluting conductance contributions at charged ferroelectric domain walls

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npj Computational Materials 2020年第1期6卷 319-325页

作者： Theodor S.Holstad Trygve M.Ræder Donald M.Evans Didirk R.Småbråten Stephan Krohns Jakob Schaab Zewu Yan Edith Bourret Antonius T.Jvan Helvoort Tor Grande Sverre M.Selbach Joshua C.agar Dennis Meier Department of Materials Science and Engineering Norwegian University of Science and Technology(NTNU)7491 TrondheimNorway Department of Materials Science and Engineering Lehigh UniversityBethlehemPA 18015USA Experimental Physics V Center for Electronic Correlations and MagnetismUniversity of Augsburg86159 AugsburgGermany Department of Materials ETH Zurich8093 ZürichSwitzerland Materials Sciences Division Lawrence Berkeley National LaboratoryBerkeleyCA 94720USA Department of Physics ETH Zurich8093 ZürichSwitzerland Department of Physics Norwegian University of Science and Technology(NTNU)7491 TrondheimNorway

Ferroelectric domain walls are promising quasi-2D structures that can be leveraged for miniaturization of electronics components and new mechanisms to control electronic signals at the *** the significant progress in experiment and theory,however,most investigations on ferroelectric domain walls are still on a fundamental level,and reliable characterization of emergent transport phenomena remains a challenging task.

关键词： walls ferroelectric conductance

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