predictive hierarchical modeling of chemical separations and transformations in functional nanoporous materials: synergy of electronic structure theory, molecular simulation, machine learning, and experiment
November 17, 2020 | Beginning January 2021, Laura Gagliardi will serve as Associate Editor of the Journal of the American Chemical Society (JACS).
November 17, 2020 | Dylan Anstine (Colina Group, University of Florida) has received an award for Excellence in Graduate Student Research (Area 08A) from the American Institute of Chemical Engineers (AIChE).
September 17, 2020 | The American Chemical Society (ACS) has named Ilja Siepmann editor-in-chief of the Journal of Chemical & Engineering Data.
NMGC researchers Alán Aspuru-Guzik (University of Toronto), Omar K. Farha, and Randall Snurr (both of Northwestern University) propose an automated nanoporous materials discovery platform powered by a supramolecular variational autoencoder for the generative design of reticular materials.
MOFs can mimic biological systems in the way they interact with molecular oxygen. Drawing inspiration from biological O2 carriers, hydroxo species have been introduced in the Co(OH)2(BBTA) MOF to stabilize cobalt(III)-superoxo species by hydrogen bonding. Additionally, O2-binding weakens in this material as a function of loading, a property called negative cooperativity. This property is typical of enzymes, but it had never been observed in extended framework materials before this study. This unprecedented behavior extends the tunable properties that can be used to design metal–organic frameworks for adsorption-based applications.
The revM11 functional is an improved version of the range-separated parameterization originally used in the M11 functional to obtain a parametrization.
This research is supported by the U.S. Department of Energy, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences and Biosciences under Award DE-FG02-17ER16362 (Predictive Hierarchical Modeling of Chemical Separations and Transformations in Functional Nanoporous Materials: Synergy of Electronic Structure Theory, Molecular Simulations, Machine Learning, and Experiment) and was previously supported by DE-FG02-12ER16362 (Nanoporous Materials Genome: Methods and Software to Optimize Gas Storage, Separations, and Catalysis).
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