Deep Learning Analysis of Defect and Phase Evolution During Electron Beam Induced Transformations in WS2

TL;DR

This paper presents a deep learning framework for analyzing real-time STEM data to identify and classify defects and phase transformations in WS2, enabling rapid, detailed insights into defect dynamics and chemical reactions at the atomic level.

Contribution

The authors developed a deep learning approach that automates defect detection and classification in STEM data, providing new capabilities for studying solid-state transformations.

Findings

Automated extraction of thousands of defects in seconds

Classification of defects into categories using unsupervised clustering

Insights into defect diffusion and transition probabilities

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.