Weston Fulton Professor of Materials Science and Engineering, University of Tennessee, Knoxville
Research focus: Direct atomic fabrication by electron beams and scanning probes. Machine learning and artificial intelligence methods in materials synthesis, discovery, and optimization. Automated experiment and autonomous imaging and characterization workflows in scanning transmission electron microscopy and scanning probes for applications including physics discovery and atomic fabrication. Mesoscopic studies of electrochemical, ferroelectric, and transport phenomena in energy and information technology materials via scanning probe microscopy. Quantum Theory and Simulations, e.g. AI, high performance computing, topology, algorithmic development, correlated systems, Quantum Information and Communications.
Website https://mse.utk.edu/people/sergei-v-kalinin/
Follow on @sergei_imaging (twitter)
LinkedIn: https://www.linkedin.com/in/sergei-kalinin-5bb44b18/
Bio
Sergei Kalinin is a professor at the University of Tennessee, Knoxville, following a year at the Amazon as a principal scientist (2022-2023) and 20 years at Oak Ridge National Laboratory. At ORNL, he was a Corporate Fellow and a group Leader at the Center for Nanophase Materials Sciences (2020-2022) and the director for the Institute for Functional Imaging of Materials (2014-2019). Sergei has co-authored >650 publications, with a total citation of >42,000 and an h-index of >105. He is a foreign member of Academia Europaea, fellow of AAIA, MRS, APS, MSA, IoP, IEEE, Foresight Institute, and AVS; a recipient of the Feynman Prize of the Foresight Institute (2022), Blavatnik Award for Physical Sciences (2018), RMS medal for Scanning Probe Microscopy (2015), Presidential Early Career Award for Scientists and Engineers (PECASE) (2009); Burton medal of Microscopy Society of America (2010); 4 R&D100 Awards (2008, 2010, 2016, and 2018); and a number of other distinctions. He received his MS degree from Moscow State University in 1998 and Ph.D. from the University of Pennsylvania (with Dawn Bonnell) in 2002.
Research Description
We explore applications of machine learning and artificial intelligence methods in materials synthesis, discovery, and optimization. The applications include physics discovery in energy and quantum materials, atomic fabrication via electron beams and scanning probe, and mesoscopic studies of electrochemical, ferroelectric, and transport phenomena via scanning probe microscopy of materials, combinatorial libraries, and operando devices. This effort is based on the development of hyper languages for description of the experiment and characterization workflows, workflow design and optimization, and implementation of automated and autonomous experiment in scanning transmission electron microscopy and scanning probes. This work is performed with multiple collaborating groups at UTK, ORNL, and academia and industry worldwide.
Recent research
https://doi.org/10.1063/PT.3.5018
https://doi.org/10.1038/s42256-022-00460-0
https://doi.org/10.1021/acsnano.2c07451
https://doi.org/10.1002/advs.202203422
https://doi.org/10.1002/adma.202201345
https://doi.org/10.1038/nmat4395
https://doi.org/10.1002/smll.201502048
10.1002/smll.201801771
https://doi.org/10.1021/acsnano.2c05303
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