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27 April 2024 |
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Article overview
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Deep Learning Analysis of Defect and Phase Evolution During Electron Beam Induced Transformations in WS2 | | Artem Maksov
; Ondrej Dyck
; Kai Wang
; Kai Xiao
; David B. Geohegan
; Bobby G. Sumpter
; Rama K. Vasudevan
; Stephen Jesse
; Sergei V. Kalinin
; Maxim Ziatdinov
; | | Date: |
14 Mar 2018 | | Abstract: | Understanding elementary mechanisms behind solid-state phase transformations
and reactions is the key to optimizing desired functional properties of many
technologically relevant materials. 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 minima, 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 structures (defects) that break a crystal lattice periodicity and
apply it for mapping solid state reactions and transformations in layered WS2
doped with Mo. This framework allows extracting thousands of lattice defects
from raw STEM data (single images and movies) 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 the 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 point defect 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. | | Source: | arXiv, 1803.5381 | | Services: | Forum | Review | PDF | Favorites | |
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