Bai, PengJeon, Mi YeongRen, LiminKnight, ChrisDeem, Michael W.Tsapatsis, MichaelSiepmann, J. Ilja2015-02-112015-02-112015Bai, Peng, Jeon, Mi Yeong, Ren, Limin, et al.. "Discovery of optimal zeolites for challenging separations and chemical transformations using predictive materials modeling." <i>Nature Communications,</i> 6, (2015) Nature Publishing Group: http://dx.doi.org/10.1038/ncomms6912.https://hdl.handle.net/1911/79024Zeolites 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.engThis is an author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by Nature Communications Group.Journal articlehttp://dx.doi.org/10.1038/ncomms6912