Metal–organic frameworks (MOFs) are an interesting class of materials synthesized in a “building-block” that enables tuning for specific applications. However, the enormous number of building blocks makes it challenging to select the best material for a given application. We describe methods using molecular simulation and machine learning to accelerate the selection of top MOFs for applications such as hydrogen storage and adsorption separations.
Aimed at those within the energy industry or those with an interest in energy storage. Delegates will gain an increased understanding of novel energy storage techniques.
Professor Randall Snurr, Northwestern University
Randall Snurr is the John G. Searle Professor and Department Chair of Chemical and Biological Engineering at Northwestern University. His research has been recognized with the Institute Award for Excellence in Industrial Gases Technology from the American Institute of Chemical Engineers and election as a corresponding member of the Saxon Academy of Sciences. His research interests include development of new nanoporous materials for energy and environmental applications, molecular simulation, machine learning, adsorption separations, diffusion in nanoporous materials, and catalysis.
The material presented in this webinar has not been peer-reviewed. Any opinions are the presenter’s own and do not necessarily represent those of IChemE or the Clean Energy SIG. The information is given in good faith but without any liability on the part of IChemE.
A one-hour online session: 40 minutes' presentation + 20 minutes' Q&A.
The presentation will be delivered via GoToWebinar®.
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