W2D2S2 - Microscopy is All You Need



Online Via Zoom


The Western Washington Data-driven Discovery Seminar Series (W2D2S2) is hosting a series of events in partnership with Pacific Northwest National Laboratory again this Fall on Thursdays from 3:00-4:00pm! Please keep an eye out and email w2d2s2organizers@gmail.com for the zoom invite link!

Keynote Speaker: Professor Sergei Kalini, University of Tennessee, Knoxville

Title: Microscopy is All You Need

Abstract: Machine learning and artificial intelligence (ML/AI) are rapidly becoming an indispensable part of physics research, with domain applications ranging from theory and materials prediction to high-throughput data analysis. Over the last several years, increasing attention is attracted to the use of AI interacting with physical system as a part of active learning – including materials discovery and optimization, chemical synthesis, and physical measurements. For these active learning problems, microscopy arguably represents an ideal model application combining aspects of materials discovery via observation and spectroscopy, physical learning with relatively shallow priors and small number of exogenous variables, and synthesis via controlled interventions. In this presentation, I will discuss recent progress in automated experiment in scanning probe and electron microscopy, ranging from feature to physics discovery via active learning. The applications of classical deep learning methods in streaming image analysis are strongly affected by the out of distribution drift effects, and the approaches to minimize though are discussed. I will further illustrate transition from post-experiment data analysis to active learning process, including learning structure-property relationships and materials discovery in composition spread libraries. Here, the strategies based on simple Gaussian Processes often tend to produce sub-optimal results due to the lack of prior knowledge and very simplified (via learned kernel function) representation of spatial complexity of the system. Comparatively, deep kernel learning (DKL) and structured Gaussian Processes methods allow to realize both the exploration of complex systems towards the discovery of structure-property relationship and enable automated experiment targeting physics (rather than simple spatial feature) discovery. The latter is illustrated via experimental discovery of ferroelectric domain dynamics in piezoresponse force microscopy. For probing physical mechanisms of tip-induced modifications, I will demonstrate the combination of the structured Gaussian process and reinforcement learning, the approach we refer to as hypothesis learning. Here, this approach is used to learn the domain growth laws on a fully autonomous microscope. The future potential of Bayesian active learning for autonomous microscopes is discussed. These concepts and methods can be extended from microscopy to other areas of automated experiment.

Bio: Sergei Kalinin is a professor at the University of Tennessee, Knoxville (currently on sabbatical at Amazon), following 20 years at Oak Ridge National Laboratory. He received his MS degree from Moscow State University in 1998 and Ph.D. from the University of Pennsylvania (with Dawn Bonnell) in 2002. His research focuses on the applications of big data and artificial intelligence methods in materials discovery and atomically resolved imaging by scanning transmission electron microscopy and scanning probes for applications including physics discovery and atomic fabrication, as well as mesoscopic studies of electrochemical, ferroelectric, and transport phenomena via scanning probe microscopy. Sergei has co-authored >650 publications, with a total citation of ~45,000 and an h-index of >100. He is a fellow of MRS, APS, IoP, IEEE, Foresight Institute, and AVS; a recipient of the 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. 

See our W2D2S2 website for more information

Please email w2d2s2organizers@gmail.com for the zoom invite link!


The Fall 2022 W2D2S2 Organizing Committee

Sarah Akers, Ying Bao, Stefan Dernbach, Brian Hutchinson, Tim Kowalczyk, Kimihiro Noguchi, Mohammad Taufique