Title: Machine Learning for Imaging Structural Properties of Energy and Quantum Materials

Speaker: Prof. Jian-min Zuo

Date/Time: 2023.6.27 10:00-11:00

Location: Yiucheng Lecture Hall (500), Xu Zuyao Building

Inviter: Asso. Prof. Wenpei Gao

Biography

Dr. Jian-Min Zuo is the Racheff Professor of Materials Science and Engineering and the Seitz Materials Research Laboratory, University of Illinois, Urbana-Champaign. Professor Jian-Min Zuo received his Ph.D. in Physics from Arizona State University (ASU) in 1989 and then took a three-year postdoctoral fellowship at the National Science Foundation center for high resolution electron microscopy and the Physics department at ASU. During this time, he pioneered the development of quantitative convergent beam electron diffraction and the study of crystal charge densities, including the mapping of d-holes in cuprite. At University of Illinois, he developed a research program on the atomistic structure of nanostructured materials, ultrafast electron diffraction and study of interfacial structure and bonding. He was instrumental in bringing the aberration corrected scanning transmission electron microscopes and an environmental dynamical transmission electron microscope to University of Illinois. He is also a leader in developing coherent electron nanodiffraction and electron diffractive imaging techniques. He is a fellow of American Physical Society and Microscopy Society of America and a recipient of Gjonnes Award from International Union of Crystallography. He is the author of Advanced Transmission Electron Microscopy book with Prof. John CH Spence of ASU.

Abstract

Transmission electron diffraction is a powerful and versatile structural probe for the characterization of a broad range of materials, from nanocrystalline thin films to single crystals. With recent developments in fast electron detectors and efficient computer algorithms, it now becomes possible to collect unprecedently large datasets of diffraction patterns (DPs) and process DPs to extract crystallographic information to form images or tomograms based on crystal structural properties, giving rise to data-driven electron microscopy. Critical to this kind of imaging is the type of crystallographic information being collected, which can be achieved with a judicious choice of electron diffraction techniques, and the efficiency and accuracy of DP processing, which requires the development of new algorithms. This talk will highlight recent progress made in scanning electron diffraction and its materials science and technology applications for strain and orientation mapping, determination of short-range ordering in high entropy alloys, imaging of polar domains, defect analysis using machine learning and imaging of molecular crystals.