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Big, Deep, and Smart Data in Energy Materials Research: Atomic View on Materials Functionalities

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SERGEI V. KALININ

Director, Institute for Functional Imaging of Materials

The Center for Nanophase Materials Sciences

Oak Ridge National Laboratory

 Abstract

The development of electron and scanning probe microscopies in the second half of XX century have produced spectacular images of internal structure and functionalities of matter with nanometer and now atomic resolution. Much of this progress since 80ies was enabled by computer-assisted methods for data acquisition and analysis that provided automated analogs of classical storage methods. However, the progress in imaging technologies since the beginning of XXI century has opened the veritable floodgates of high-veracity information on atomic positions and functionality, often in the form of multidimensional data sets containing partial or full information on atomic positions, functionalities, etc. In this presentation, I will discuss the research activity coordinated by the Institute for Functional Imaging of Materials (IFIM), namely pathways to bridge imaging and theory via big data technologies to enable design of new materials with tailored functionalities. This goal will be achieved first through a big data approach – i.e., developing pathways for full information retrieval and exploring correlations in structural and functional imaging. In Scanning Probe Microscopy, this approach is illustrated via full information capture in SPM based on recording and complete analysis of data stream from photodetector. This general-mode (G-Mode) SPM is illustrated for classical SPM modes such as intermittent contact mode SPM, as well as piezoresponse force microscopy and spectroscopy (PFM) and Kelvin probe microscopy. The analysis of the information contact allows deducing in which cases classical signal processing allows unbiased representation of the tip-surface interactions and which it incurs significant information loss. The approaches for full mapping on frequency responses providing complete view of tip-surface interactions are discussed. In electron microscopy, the big data approaches are illustrated by full data acquisition in ptychography and real-space crystallographic mapping. These techniques can be further extended to develop structure property relationships on atomic levels, creating a library of atomic configurations and associated properties. A deep data approach will allow merging this knowledge with physical models, providing input into the Materials Genome program and enabling a new paradigm for materials research based on theory-experiment matching of microscopic degrees of freedom. Finally, a smart data approach will enable algorithms for data identification, expert assessment, and ultimately, control over matter. I will further discuss the extension of similar approaches to mesoscopic imaging and imaging in information domain.

Biography

Sergei V. Kalinin is the director of the ORNL Institute for Functional Imaging of Materials and distinguished research staff member at the Center for Nanophase Materials Sciences (CNMS) at Oak Ridge National Laboratory, as well as a theme leader for Electronic and Ionic Functionality on the Nanoscale (at ORNL since 2002). He also holds a Joint Associate Professor position at the Department of Materials Science and Engineering at the University of Tennessee-Knoxville, and an Adjunct Faculty position at Pennsylvania State University. His research interests include application of big data, deep data, and smart data approaches in atomically resolved and mesoscopic imaging to guide the development of advanced materials for energy and information technologies, as well as coupling between electromechanical, electrical, and transport phenomena on the nanoscale.

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

Status

  • Workflow Status:Published
  • Created By:Cecelia Jones
  • Created:05/14/2016
  • Modified By:Fletcher Moore
  • Modified:04/13/2017

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