1:30 PM - *GI01.02.01
Knowledge from Atomically Resolved Images—Deep Learning Meets Statistical Physics
Sergei Kalinin1,Stephen Jesse1,Christopher Nelson1,Maxim Ziatdinov1,Rama Vasudevan1,Ondrej Dyck1,Andrew Lupini1
Oak Ridge National Laboratory1
Atomically resolved imaging techniques including scanning transmission electron microscopy and scanning tunneling microscopy are almost routine by now and provide atomically-resolved pictures of static structures and their evolution with time, as well as insight into local electronic properties. However, the wealth of information stored within these images is still not fully harnessed. The use of this data for predictive materials design can often be broken into a two-part problem, with the first being the feature extraction, including mapping all atomic coordinates, isolating the defects, classifying the symmetry, etc. In many or all these areas, deep learning provides a robust tool that can be used in near real-time on atomically resolved images and can be trained on simulated images without need for expensive experiments to capture large training sets [1-3]. In parallel, once the local atomic configurations are identified, the question becomes how to use such information to understand the system and its interactions, and ultimately use them to build models with predictive capabilities. One answer is based on mesoscopic model matching, where the material properties are described by the corresponding Ginzburg-Landau (GL) free energy. The corresponding analytical solutions for well-defined defects including domain boundaries, surfaces, or interfaces can then be fitted to STEM data, providing information on (poorly known) gradient terms and boundary conditions. For discrete systems, we propose that by studying the characteristic structural and chemical fluctuations that exist within a single chemical composition, we can infer the relevant interactions and produce a generative model that can predict properties over a range of scales in a finite region of chemical and temperature space. We use the compositional and structural fluctuations in the quenched (static) system to build a generative model encoding the effective interactions in the system. Finally, we extend this machine learning approach to the mapping of solid-state reaction mechanisms. We developed a deep learning approach that allows fully automated identification of individual atoms in STEM images, using theoretical or labeled images as a training set. We extend this approach to construct reaction pathways for point defects in 2D materials, trace the structural evolution of atomic species during electron beam manipulation, and create a library of defect configurations in Si- and vacancy doped graphene.
This research is supported by the by the U.S. Department of Energy, Basic Energy Sciences, Materials Sciences and Engineering Division and the Center for Nanophase Materials Sciences, which is sponsored at Oak Ridge National Laboratory by the Scientific User Facilities Division, BES DOE.
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