Four Georgia Tech research hubs have launched a new “chalk & talk” brown bag lunch series on Big Data. The weekly series, sponsored jointly by the Institute for Data & High Performance Computing (IDH), Institute for Materials (IMaT), Center for Data Analytics (CDA) and Center for High Performance Computing (HPC) will be held on most Thursdays during the Fall and Spring Semesters and feature a mix of topics, including those related to big data for materials and manufacturing, as well as other topics critical to the broader area of big data.

All meetings are held on Thursdays during lunchtime. 

Date: January 9
Topic: “Quantification and Low Dimensional Representation of Material Internal Structure Using Spatial Correlations”
Presenters(s): Surya R. Kalidindi
Abstract:
Almost all materials that relate to advanced technologies exhibit a richness of hierarchical internal structures at multiple length scales (spanning from atomic to macroscale). Certain salient features of this structure control the performance characteristics of interest for a selected application. Although there is often some intuition about what these salient features might be, validated protocols do not yet exist for reliably identifying these features. Further, efficient computational protocols do not yet exist for tracking their evolution during the various unit processing/synthesis steps employed in the industrial manufacture of new products/devices. In fact, the optimization of the material structure resulting in improved performance of engineering components is often the main motivation behind all activities in the field of materials science and engineering. Despite its important role, a unified computational framework for the quantification of the material hierarchical structure does not exist currently.  In this talk, I will describe a rigorous theoretical framework developed by my research group for the stochastic quantification of the material structure at any selected length scale, utilizing spatial correlations as the central metrics. This framework combines the use of n-point spatial correlations (or n-point statistics) and principal component analyses to arrive at objective, low-dimensional, representations of material internal structure in establishing core materials knowledge systems.  
Bio:
After earning a Ph.D. in mechanical engineering from the Massachusetts Institute of Technology in 1992, Surya R. Kalidindi joined the Department of Materials Science and Engineering at Drexel University. In 2013, his research group moved to the George W. Woodruff School at the Georgia Institute of Technology, where he has courtesy appointments in the School of Materials Science and Engineering and in the School of Computational Science and Engineering. Kalidindi’s research efforts over the past two decades have made seminal contributions to the fields of crystal plasticity and microstructure design. His current research is directed at developing and validating new data science enabled workflows for the successful realization of accelerated and cost-effective development of enhanced performance materials for advanced technologies.