Causal analysis of competing atomistic mechanisms in ferroelectric materials from high-resolution scanning transmission electron microscopy data  

作　　者：Maxim Ziatdinov Christopher T.Nelson Xiaohang Zhang Rama K.Vasudevan Eugene Eliseev Anna N.Morozovska Ichiro Takeuchi Sergei V.Kalinin 

机构地区：[1]The Center for Nanophase Materials Sciences,Oak Ridge National Laboratory,Oak Ridge,TN 37831,USA [2]Computational Sciences and Engineering Division,Oak Ridge National Laboratory,Oak Ridge,TN 37831,USA [3]Department of Materials Science and Engineering,University of Maryland,College Park,MD 20742,USA [4]Institute for Problems of Materials Science,National Academy of Sciences of Ukraine,Krjijanovskogo 3,Kyiv 03142,Ukraine [5]Institute of Physics,National Academy of Sciences of Ukraine,46,pr.Nauky,Kyiv 03028,Ukraine

出　　处：《npj Computational Materials》2020年第1期570-578,共9页计算材料学（英文）

基　　金：The work at the University of Maryland was supported in part by the National Institute of Standards and Technology Cooperative Agreement 70NANB17H301;the Center for Spintronic Materials in Advanced infoRmation Technologies(SMART)one of centers in nCORE,a Semiconductor Research Corporation(SRC)program sponsored by NSF and NIST;A.N.M.work was partially supported by the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie(grant agreement No 778070).

摘　　要：Machine learning has emerged as a powerful tool for the analysis of mesoscopic and atomically resolved images and spectroscopy in electron and scanning probe microscopy,with the applications ranging from feature extraction to information compression and elucidation of relevant order parameters to inversion of imaging data to reconstruct structural models.However,the fundamental limitation of machine learning methods is their correlative nature,leading to extreme susceptibility to confounding factors.Here,we implement the workflow for causal analysis of structural scanning transmission electron microscopy(STEM)data and explore the interplay between physical and chemical effects in a ferroelectric perovskite across the ferroelectric–antiferroelectric phase transitions.

关 键 词：FERROELECTRIC analysis FOUNDING 

分 类 号：TH742[机械工程—光学工程]