 |
Center for Nonlinear Studies |
This presentation explores the application of statistical learning techniques unraveling the complex relationships between structure, bonding and chemistry in inorganic materials – for example, identifying pathways that demonstrate how parameters describing electronic structure, chemistry and crystal geometry “communicate” with each other to ultimately define properties. We show how by integrating electronic and crystal geometry information into both classification and predictive data mining techniques, one can extract complex rule based design strategies for materials. In this presentation we also discuss how statistical learning techniques can be used to augment more classical approaches to computational based design of materials.
Host: Turab Lookman, T-4