Predictive Modeling of Nanoporous Materials: High-Throughput Screening, Machine Learning, and First Principles Simulations

Event
Friday, October 7, 2022 - 1:10pm
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Dr. J. Ilja Siepmann, Department of Chemistry and Chemical Theory Center and the Department of Chemical Engineering and Materials Science, University of Minnesota

Host: Dr. Potoyan

Nanoporous materials, such as zeolites and metal-organic frameworks, play numerous important roles in modern oil and gas refineries and have the potential to advance the production of fuels and chemical feedstocks from renewable resources. The performance of a nanoporous material as separation medium and catalyst depends on its framework structure and the type or location of active sites. Hence, identification of optimal nanoporous materials 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 nanoporous materials for ethanol purification from aqueous solution and for the hydroisomerization of linear to slightly branched alkanes with 18-30 carbon atoms. Deep neural networks trained on large simulation data are utilized to make continuous predictions that allow for finding optimal process conditions for a given nanoporous adsorbent or to find optimal adsorbents for a given set of process conditions. First principles Monte Carlo simulations where the potential energy is calculated on-the-fly using Kohn-Sham density functional theory, allow for the prediction of highly selective adsorption of gas molecules in metal-organic frameworks with under-coordinated metal nodes and of reaction equilibria in cation-exchanged zeolites.

J. Ilja Siepmann is a Professor of Chemistry, Distinguished McKnight University Professor, a Distinguished Teaching Professor, and member of the graduate faculties in chemical physics, chemical engineering, and materials science at the University of Minnesota. He is also the director of the DOE-funded Nanoporous Materials Genome Center and the editor-in-chief for the Journal of Chemical and Engineering Data. He received his Ph.D. in Chemistry from the University of Cambridge. Before joining the University of Minnesota in 1994, Dr. Siepmann carried out postdoctoral research at the IBM Zurich Research Laboratory, the Royal/Shell Laboratory in Amsterdam, and the University of Pennsylvania’s Laboratory for the Research on the Structure of Matter. His scientific interests are focused on particle-based simulations of complex chemical systems, including the prediction of phase and sorption equilibria and of thermophysical properties, the understanding of retention in chromatography, and the investigation of microheterogeneous fluids and nucleation phenomena. His research efforts have advanced the capabilities of molecular simulations through the development of efficient Monte Carlo algorithms and transferable force fields.

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