Abstract
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 WS2. 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 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.
Original language | English |
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Article number | 12 |
Journal | npj Computational Materials |
Volume | 5 |
Issue number | 1 |
DOIs | |
State | Published - Dec 1 2019 |
Funding
Research was conducted at the Center for Nanophase Materials Sciences, which is a DOE Office of Science User Facility. R.V.K. and S.V.K. were supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division. The synthesis of Mo-doped WS2 2D materials (K.W., K.X., D. G.) was supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division. M.Z., O.D., S.J. acknowledge support by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the U.S. Department of Energy. A.M. acknowledges fellowship support from the UT/ORNL Bredesen Center for Interdisciplinary Research and Graduate Education.