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Workshop: AI for Accelerated Materials Design (AI4Mat)

Automated Materials Characterization Keynote

Huolin Xin


Abstract: Deep learning schemes have already impacted areas such as cognitive game theory (e.g., computer chess and the game of Go), pattern (e.g., facial or fingerprint) recognition, event forecasting, and bioinformatics. They are beginning to make major inroads within physics, chemistry and materials sciences and hold considerable promise for accelerating the discovery of new theories and materials. In this talk, I will introduce deep convolutional neural networks and how they can be applied to the computer vision problems in transmission electron microscopy and tomographic imaging. Bio: Huolin Xin is a full professor at UC Irvine. He graduated from the Physics Department of Cornell University in 2011 and joined the University of California, Irvine in 2018. Prior to becoming a professor at UCI, he worked at Brookhaven National Laboratory as a scientific staff member and a principal investigator from 2013 to 2018. His research has resulted in more than 280 peer-reviewed publications (h-index 73 and citations 24,600). He received the MRS Oustanding Early Career Investigator Award, MSA Burton Medal, DOE Early Career Award, and the UCI Distinguished Early-Career Faculty for Research in 2020. He was the Chair of the largest international electron microscopy conference, Microscopy and Microanalysis, in 2020. His work on battery materials has been selected as the 2020, 2019 and 2014's Top-10 Scientific Achievements by Brookhaven Lab.

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