CoSMIC is an international collaborative research program focused on data driven discovery in materials science. Its central research theme is to develop new computational and experimental ways of accelerated mechanistic based discovery and design of materials using informatics methods. CoSMIC was established seven years ago through the International Materials Institute program of the US National Science Foundation. It is now continuing through support from a number of agencies including NSF, AFOSR, DARPA, ONR and industry. The program is directed by Professor Krishna Rajan of Iowa State University and involves a network of laboratories in over ten countries. Contact: Professor Krishna Rajan Iowa State University Department of Materials Science and Engineering & Bioinformatics and Computational Biology Program Hoover Hall; Ames, IA Phone: OVERVIEW

Krishna Rajan Graduate Students: Prasanna V. Balachandran Wei Hu Kaustubh Kaluskar Gaurav Mohanty Santosh K. Suram Susan Vander Plas Ryan Vander Plas Undergraduate Students: Bradley Williams: Mat.Sci.Eng. Briana Kelly: Mat.Sci.Eng. Disha Labhasetwar: Honors Freshman Elease McLaurin: Ind. Manuf. Sys. Eng. Samuel Reeve: Honors Freshman Rebecca Tabbert: NSF-REU program David Harrison: NSF-REU program Laboratory Managers: Dr. C. Mosher Roy J. Carver Laboratory for Ultrahigh Resolution Biological Microsopy Dr. M. Stukowski W.M. Keck Laboratory for the Fabrication of Microminiaturized Analytical Instrumentation Post Doctoral and Visiting Scientists: Dr. Scott Broderick Dr. Chang Sun Kong Dr. Joaquin Peralta Dr. Claudia Loyola Dr. Wei Luo MEMBERS

Statistical Learning: Exploring the efficacy and computational strategies of statistical learning methods for a variety of materials science platforms. Materials Modeling: Establishing informatics as a "third leg" to be integrated into experimental and computational materials sciences for multi-scale materials design and discovery. Atom Probe Tomography: Advancing quantitative interpretation of atom probe tomography and development of in-situ experimentation. Nano-informatics: Applying informatics for the advancement of nanoscience including the analysis of nanoscale phenomena and the rational design of nanomaterials. Combinatorial Experimentation: integrating informatics methods into combinatorial and high throughput screening methods to ensure knowledge discovery and not just data discovery RESEARCH

RESEARCH : Statistical learning Statistical Learning: A core tool for our research is the use of statistical learning methods such as data dimensionality reduction, self organization maps, clustering analysis, and recursive partioning methods combined with advanced scientific visualization methods. These techniques permit accelerated but robust approaches for materials discovery. Attach: Fig_Research1

RESEARCH : Materials Modeling Modeling and Design: QSAR for Materials Engineering A major aspect of our research is to link statistical learning methods to physically based modeling strategies with experimental data to develop predictive multi-scale models, identify new structure property relationships ( “quantitative structure-activity relationships”- QSARs as is used organic chemistry and drug discovery) and discover new and yet untested materials. Attach: Fig_Research2

RESEARCH : Atom Probe Tomography Atom Probe Tomography Our research is focused on advancing quantitative interpretation of atom probe images and mass spectra as well as developing new experimental tools for in-situ atom probe tomography. Attach: Fig_Research3

RESEARCH : Nanoinformatics Attach: Fig_Research4 Nanoinformatics This aspect of the program explores how informatics can be used to elucidate nanoscale mechanisms in materials, develop a rational design strategy for new nanomaterials and enhance the quantitative analysis of spectral and imaging data at the nanoscale. Applications of the research include, discovering new nanocluster chemistries of materials, extracting pico-scale information from high resolution imaging and other characterization techniques and integrating nanomaterial data curation with informatics.

Combinatorial Experimentation The aim of this thrust of our program is to link combinatorial experimentation with informatics methods to convert the data from high-throughput experimentation to high-throughput knowledge discovery. Our work is focused on a variety of materials discovery platforms including, polymeric drug delivery materials, inorganic multicomponent catalysts and biological microarrays. RESEARCH : Combinatorial Experimentation Attach: Fig_Research5

Research Network - International Attach: ResearchNetwork-international

Research Network – United States Attach: ResearchNetwork-US

International Workshops Attach: Workshops