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

Event Sponsor: 
Computing, Environment, and Life Sciences Seminar
Start Date: 
Sep 22 2015 - 1:00pm
Building/Room: 
Building 241/Room D172
Location: 
Argonne National Laboratory
Speaker(s): 
Sergei V. Kalinin
Speaker(s) Title: 
Oak Ridge National Laboratory
Host: 
Emine Begum Gulsoy

Structural and electronic properties of oxide surfaces control their physical functionalities and electrocatalytic activity, and are currently of interest for energy generation and storage applications. In this presentation, I will discuss several examples of high-resolution studies of the electronic and electrochemical properties of oxide surfaces enabled by multidimensional scanning probe microscopies. On the mesoscopic scale, combination of strain- and current sensitive scanning probe microscopies allows to build nanometer-scale maps of local reversible and irreversible electrochemical activities. The use of multivariate statistical methods allows separating the complex multidimensional data sets into statistically significant components which in certain cases can be mapped onto individual physical mechanisms. I will further discuss the use of in-situ Pulsed Laser Deposition growth combined with atomic resolution Scanning Tunneling Microscopy and Spectroscopy to explore surface structures and electrochemical reactivity of oxides on the atomic scale. For SrRuO3, we directly observe multiple surface reconstructions and link these to the metal-insulator transitions as ascertained by UPS methods. On LaxCa1-xMnO3, we demonstrate strong termination dependence of electronic properties and presence of disordered oxygen ad-atoms. The growth dynamics and surface terminations of these films are discussed, along with single-atom electrochemistry experiments performed by STM. Finally, I explore the opportunities for atomically-resolved imaging and property data mining of functional oxides extending beyond classical order parameter descriptions, and giving rise to the deep data analysis in materials research.

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