Probing atomic-scale symmetry breaking by rotationally invariant machine learning of multidimensional electron scattering  

作　　者：Mark P.Oxley Maxim Ziatdinov Ondrej Dyck Andrew R.Lupini Rama Vasudevan Sergei V.Kalinin 

机构地区：[1]Center for Nanophase Materials Sciences,Oak Ridge National Laboratory,Oak Ridge,TN,USA [2]Computational Sciences and Engineering Division,Oak Ridge National Laboratory,Oak Ridge,TN,USA

出　　处：《npj Computational Materials》2021年第1期595-600,共6页计算材料学（英文）

基　　金：This research used resources of the Compute and Data Environment for Science(CADES)at the Oak Ridge National Laboratory,which is supported by the Office of Science of the U.S.Department of Energy under Contract No.DE-AC05-00OR22725.

摘　　要：The 4D scanning transmission electron microscopy(STEM)method maps the structure and functionality of solids on the atomic scale,yielding information-rich data sets describing the interatomic electric and magnetic fields,structural and electronic order parameters,and other symmetry breaking distortions.A critical bottleneck is the dearth of analytical tools that can reduce complex 4D-STEM data to physically relevant descriptors.We propose an approach for the systematic exploration of 4D-STEM data using rotationally invariant variational autoencoders(rrVAE),which disentangle the general rotation of the object from other latent representations.The implementation of purely rotational rrVAE is discussed as are applications to simulated data for graphene and zincblende structures.The rrVAE analysis of experimental 4D-STEM data of defects in graphene is illustrated and compared to the classical center-of-mass analysis.This approach is universal for probing symmetry-breaking phenomena in complex systems and can be implemented for a broad range of diffraction methods.

关 键 词：symmetry BREAKING ROTATIONAL 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]