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Algorithms for Electron Microscopy: Distilling Imaging Data for Studies of Nanomaterial Evolution

Mary Scott

Ted van Duzer Associate Professor, Materials Science and Engineering, University of California, Berkeley

Event Details:

Wednesday, October 19, 2022
11:30am - 1:20pm PDT

Location

Stanford University
McCullough Building, Room 115
476 Lomita Mall
Stanford, CA 94305
United States

This event is open to:

Alumni/Friends
Faculty/Staff
General Public
Students

Abstract:  Recently, materials science has undergone a data science revolution. With the increasing application of advanced computational methods for analysis of experimental data streams, the development of advanced algorithms for data distillation is an important theme in modern materials science research. Electron microscopy is the characterization method of choice to observe the atomic-scale and microstructural local features within materials that play a critical role in material performance. With resolution that can be deeply sub-Angstrom, a single image from a high-resolution electron microscope can measure atomic positions, defects, and strain, but only in two dimensions. Extending atomic resolution electron microscopy to three dimensions via tilt axis tomography allows one to determine the 3D atomic structure of a material without averaging or using a priori information. Enabled by advanced 3D reconstruction algorithms, this method has been used to isolate crystalline grains in 3D, to visualize the atomic arrangement of atoms in defects, and to localize individual atoms of a sample with 20 pm precision. Extracting quantitative information from the complex datasets, which can describe over 20,000 atomic positions- requires new methods of data analysis. Ultimately, data reconstruction and quantification pipelines enable determination of 3D dislocation and strain fields, 3D atomic species mapping, and resolving complex crystal grain structures. When applied to materials transformation, for example those that take place during solution phase synthesis, atomic electron tomography gives unique insight into complex and transient nanomaterial structures. While atomic electron tomography can give insight into individual atomic positions, a full understanding of nanomaterial transformations requires population-wide studies. The increasing ability to perform high throughput electron microscopy has created opportunity for large scale nanomaterial studies alongside a need for robust, automated analysis. Advances in machine learning and computer vision have made high accuracy automated image interpretation possible. While widely applied to natural images, this approach is only recently being applied to atomic resolution electron microscopy images. Therefore, it is desirable to establish how to best implement machine learning approaches for scientific imaging data analysis. When combined with existing automatic image acquisition protocols, machine learning is now a viable option to close the materials design loop and incorporate electron microscopy into high-throughput materials design and synthesis.

Mary Scott picture

Bio: Mary Scott is the Ted van Duzer Associate Professor in the Materials Science and Engineering department at University of California, Berkeley. She is also a Faculty Staff Scientist at the National Center for Electron Microscopy, part of the Molecular Foundry at Lawrence Berkeley National Lab. She received a B.S. in Aerospace Engineering and a B.S. in Physics, followed by an M.S. in Physics, from North Carolina State University. She obtained her Ph.D. in Physics from the University of California, Los Angeles. Prof. Scott’s research program seeks to combine advanced electron microscopy with modern mathematical approaches for data handling and interpretation. Examples of her work include atomic resolution electron tomography studies of nanomaterials, machine learning approaches to interpret imaging and diffraction electron microscopy data, scanning nanodiffraction studies of disordered materials, and multimodal studies of interfaces in battery materials.

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