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Physical Discovery by Machine Learning: from Symmetries and Chemical Reactions to Generative and Causal Models

Program in Applied Mathematics Colloquium

Physical Discovery by Machine Learning: from Symmetries and Chemical Reactions to Generative and Causal Models
Series: Program in Applied Mathematics Colloquium
Location: ONLINE
Presenter: Sergei Kalinin, Oak Ridge National Laboratory, Corporate Fellow, Group Leader for Data Nano Analytics Group (DNA), The Center for Nanophase Materials Sciences

Machine learning has emerged as a powerful tool for the analysis of mesoscopic and atomically resolved images and spectroscopy in electron and scanning probe microscopy. The applications ranging from feature extraction to information compression and elucidation of relevant order parameters to inversion of imaging data to reconstruct structural models have been demonstrated. In this presentation, I will discuss several applications of autoencoders and variational autoencoders for the analysis of image and spectral data in STEM and SPM. The special emphasis is made on the rotationally invariant variational autoencoders that allow to disentangle rotational degrees of freedom from other latent variables in imaging and spectral data. The analysis of the latent space of autoencoders further allows establishing physically relevant transformation mechanisms. Extension of encoder approach towards establishing structure-property relationships will be illustrated on the example of ferroelectric domain walls and plasmonic structures. I will further illustrate the applications of the Bayesian inference methods towards inferring the mesoscopic and atomistic physics of materials in terms of continuous and atomistic generative models, and illustrate the pathways towards incorporation of physical models as priors within Bayesian optimization towards effective sampling of experimental parameter spaces. Ultimately, we seek to answer the causal questions such as whether frozen atomic disorder drives the emergence of the local structural distortions or average shift of the Fermi level induces structural reconstruction that in turn drive cation distribution, whether the nucleation spot of phase transition can be predicted based on observations before the transition, and what is the driving forces controlling the emergence of unique functionalities in quantum materials.

This research is supported by the by the U.S. Department of Energy, Basic Energy Sciences, Materials Sciences and Engineering Division and the Center for Nanophase Materials Sciences, which is sponsored at Oak Ridge National Laboratory by the Scientific User Facilities Division, BES DOE.

About Sergei V. Kalinin: Sergei Kalinin is a corporate fellow and a group leader at the Center for Nanophase Materials Sciences 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 presently focuses on the applications of big data and artificial intelligence methods in 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 >33,000 and an h-index of >94. 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.

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