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Nat Commun. 2015 Jan 21;6:5912. doi: 10.1038/ncomms6912.

Discovery of optimal zeolites for challenging separations and chemical transformations using predictive materials modeling.

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Departments of Chemistry and of Chemical Engineering and Materials Science and Chemical Theory Center, University of Minnesota, 207 Pleasant Street S.E., Minneapolis, Minnesota 55455, USA.
Leadership Computing Facility, Argonne National Laboratory, 9700 S. Cass Avenue, Argonne, Illinois 60439, USA.
Departments of Bioengineering and of Physics and Astronomy, Rice University, 6100 Main Street, Houston, Texas 77005, USA.


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. To date, 213 framework types have been synthesized and >330,000 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: the purification of ethanol from fermentation broths and the hydroisomerization of alkanes with 18-30 carbon atoms encountered in petroleum refining. These results demonstrate that predictive modelling 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.

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