Deep learning analysis of defect and phase evolution during electron beam-induced transformations in WS_(2)

Recent advances in scanning transmission electron microscopy(STEM)allow the real-time visualization of solid-state transformations in materials,including those induced by an electron beam and temperature,with atomic resolution.However,despite the ever-expanding capabilities for high-resolution data acquisition,the inferred information about kinetics and thermodynamics of the process,and single defect dynamics and interactions is minimal.This is due to the inherent limitations of manual ex situ analysis of the collected volumes of data.To circumvent this problem,we developed a deep-learning framework for dynamic STEM imaging that is trained to find the lattice defects and apply it for mapping solid state reactions and transformations in layered WS_(2).The trained deep-learning model allows extracting thousands of lattice defects from raw STEM data in a matter of seconds,which are then classified into different categories using unsupervised clustering methods.We further expanded our framework to extract parameters of diffusion for sulfur vacancies and analyzed transition probabilities associated with switching between different configurations of defect complexes consisting of Mo dopant and sulfur vacancy,providing insight into pointdefect dynamics and reactions.This approach is universal and its application to beam-induced reactions allows mapping chemical transformation pathways in solids at the atomic level.

Learning surface molecular structures via machine vision

Recent advances in high resolution scanning transmission electron and scanning probe microscopies have allowed researchers to perform measurements of materials structural parameters and functional properties in real space with a picometre precision.In many technologically relevant atomic and/or molecular systems,however,the information of interest is distributed spatially in a non-uniform manner and may have a complex multi-dimensional nature.One of the critical issues,therefore,lies in being able to accurately identify(‘read out’)all the individual building blocks in different atomic/molecular architectures,as well as more complex patterns that these blocks may form,on a scale of hundreds and thousands of individual atomic/molecular units.Here we employ machine vision to read and recognize complex molecular assemblies on surfaces.Specifically,we combine Markov random field model and convolutional neural networks to classify structural and rotational states of all individual building blocks in molecular assembly on the metallic surface visualized in high-resolution scanning tunneling microscopy measurements.We show how the obtained full decoding of the system allows us to directly construct a pair density function—a centerpiece in analysis of disorderproperty relationship paradigm—as well as to analyze spatial correlations between multiple order parameters at the nanoscale,and elucidate reaction pathway involving molecular conformation changes.The method represents a significant shift in our way of analyzing atomic and/or molecular resolved microscopic images and can be applied to variety of other microscopic measurements of structural,electronic,and magnetic orders in different condensed matter systems.

Author Correction:Deep learning analysis of defect and phase evolution during electron beam-induced transformations in WS_(2)

The original version of the published Article omitted a statement from the Acknowledgements section.The Acknowledgements have been updated to include the following:The work on microscopy and synthesis was supported by the U.S.Department of Energy,Office of Science,Basic Energy Sciences,Materials Sciences and Engineering Division(R.K.V.,S.V.K.,K.W.,K.X.,D.G.).The HTML and PDF versions of the Article have been corrected.