(260a) Discovery of Optimal Zeolites and Metal-Organic Frameworks for Challenging Separations and Chemical Conversions through Predictive Materials Modeling Conference: AIChE Annual MeetingYear: 2015Proceeding: 2015 AIChE Annual MeetingGroup: Materials Engineering and Sciences DivisionSession: Accelerated Discovery and Development of Inorganic Materials Time: Tuesday, November 10, 2015 - 8:30am-8:55am Authors: Siepmann, J. I., University of Minnesota Bai, P., University of Minnesota Haldoupis, E., University of Minnesota Vogiatzis, K. D., University of Minnesota Deem, M. W., Rice University Tsapatsis, M., University of Minnesota Gagliardi, L., University of Minnesota Zeolites play numerous important roles in modern petroleum refineries and have the potential to advance the production of fuels and chemical feedstocks from renewable resources. The performance of a zeolite as separation medium and catalyst depends on its framework structure and the type or location of active sites. To date, more than 200 framework types have been synthesized and more than 330000 thermodynamically accessible zeolite structures have been predicted. Hence, identification of optimal zeolites for a given application from the large pool of candidate structures is attractive for accelerating the pace of materials discovery. Here we identify, through a large-scale, multi-step computational screening process, promising zeolite structures for two energy-related applications: (i) with the ability to purify ethanol beyond the ethanol/water azeotropic concentration in a single separation step from fermentation broths and (ii) with up to two orders of magnitude better adsorption capability than current technology for linear and slightly branched alkanes with 18-30 carbon atoms encountered in petroleum refining. Furthermore, this talk will discuss computational approaches for mining the CoRE-MOF database and for developing force fields from first principles that allow to find promising MOFs with uncoordinated metal sites and to predict their performance. These results demonstrate that predictive modeling and data-driven science can now be applied to solve some of the most challenging separation problems involving highly non-ideal mixtures and highly articulated compounds.