Deep Data in Materials Characterization
August 9-10, 2016
Organizer(s): Alex Belianinov
The growing availability and volumes of data – new detector technologies, metadata from electronic notebooks, instrument sensors, open web sources, and many others – as well as novel processing and analytical techniques will transform the practice of science. These new information sources can offer new insights into the structure and function of materials, specifically the interaction and origin of these properties.
In this workshop, we will present and discuss emerging methodology and analysis techniques to extract, correlate and interpret multiple information channels from a variety of analytical techniques. Special attention will be given to Scanning Probe Microscopy, (SPM) Scanning Transmission Electron Microscopy, (STEM) Raman, and Secondary Ion Mass Spectrometry (SIMS). An underlying theme for experimental techniques will revolve around multimodal imaging and combinatorial techniques involving multiple measurements in a given experimental area.
Additionally, emerging work in analysis techniques and integration of High Performance Computing (HPC) into the scientific workflow, with a focus on signal de-mixing, data co-registration, data visualization and reduction, will be presented. The workshop will include hands-on demonstrations of data analysis software designed for functional fitting, image processing, and data registration executed on Oak Ridge Leadership Computing Facility (OLCF) compute resources such as Compute and Data Environment for Science (CADES) and Titan.
Finally, this workshop aims to identify areas where advanced data analytics will significantly increase the scientific impact and the quality of the extracted information. The workshop will consist of plenary lectures to introduce the key areas of interest, contributed talks and posters, as well as discussions and/or breakout sessions to identify the needs and opportunities in the various areas.