摘要
Electron,optical,and scanning probe microscopy methods are generating ever increasing volume of image data containing information on atomic and mesoscale structures and functionalities.This necessitates the development of themachine learning methods for discovery of physical and chemical phenomena from the data,such as manifestations of symmetry breaking phenomena in electron and scanning tunneling microscopy images,or variability of the nanoparticles.Variational autoencoders(VAEs)are emerging as a powerful paradigm for the unsupervised data analysis,allowing to disentangle the factors of variability and discover optimal parsimonious representation.Here,we summarize recent developments in VAEs,covering the basic principles and intuition behind the VAEs.
基金
supported by the US Department of Energy,Office of Science,Office of Basic Energy Sciences,as part of the Energy Frontier Research Centers program:CSSAS-The Center for the Science of Synthesis Across Scales-under Award No.DE-SC0019288,located at University of Washington,DC
Additional support for ongoing pyroVED software development came from the Laboratory Directed Research and Development Program at Pacific Northwest National Laboratory(PNNL),a multiprogram national laboratory operated by Battelle for the U.S.Department of Energy
supported by the Center for Nanophase Materials Sciences(CNMS),which is aUS Department of Energy,Office of Science User Facility at Oak Ridge National Laboratory(ORNL).
