Voiland School of Chemical Engineering and Bioengineering Graduate Seminar Series
About the event
The Gene and Linda Voiland School of Chemical Engineering and Bioengineering is hosting a seminar presented by Dr. Aurora Clark, Director for the Center for Institutional Research Computing (CIRC) and Professor of Chemistry.
Aurora Clark is the Director for the Center for Institutional Research Computing and Professor of Chemistry at Washington State University. She received her Ph.D. in Physical Chemistry from Indiana University in 2003. Dr. Clark studied the electronic structure of chemical systems containing highly correlated f-block elements as a post-doctoral researcher at Los Alamos National Laboratory. She joined the Chemistry Department at WSU in 2005 and is now a Full Professor. Her research employs both quantum and statistical mechanics to study extreme and complex chemical environments, with an emphasis upon solution chemistry and liquid interfaces. She has pioneered new theoretical approaches for data fusion and analysis within molecular simulations in HPC environments. These interests are reflected in her role as Deputy Director of the Department of Energy, Energy Frontier Research Center on Interfacial Dynamics in Radioactive Environments and Materials. Dr. Clark has received several awards for scientific achievement and leadership, including being elected a Fellow of the ACS in 2017. She is currently a member of the editorial advisory board for the Journal of Chemical Physics, the co-chair of the DOE Council on Chemical Sciences, Geosciences and Biosciences and is a member of the National Academy of Sciences Committee on a New Era of Separations Science.
A Hierarchical Understanding of Separations Processes – From Quantum Chemistry to Statistical Mechanics and Data Analytics
Chemical separations span a myriad of processes – however those that involve liquids are often uniquely sensitive to molecular level detail that in turn is manifested across length-scales toward bulk measurements of efficiency and selectivity. Therein, different modeling and simulation approaches (from quantum to statistical mechanics) can be used to understand the fundamental driving forces and physics behind a successful separation. Such studies are generally based upon model systems, but as chemical complexity is increased toward realistic simulations conditions, unexpected correlations in variables emerge. Thus, a third branch of science must be pursued – one based upon data exploration that can be used to understand the effects of non-ideality, predict, and design optimal separations processing conditions. This seminar will illustrate specific case studies where the integration of quantum and statistical mechanics simulations with data analytics is providing a fundamentally new understanding of the mechanisms and forces that underlie successful chemical separations.