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. They demonstrate the automated design process with a class of metal–organic framework (MOF) structures and the goal of separating carbon dioxide from natural gas or flue gas. The autoencoder has a promising optimization capability when jointly trained with multiple top adsorbent candidates identified for superior gas separation. MOFs discovered here are strongly competitive against some of the best-performing MOFs/zeolites ever reported. Illustration by Ella Marushchenko.
Published in Nat. Mach. Intell., (2021). DOI: 10.1038/s42256-020-00271-1
Metal-organic frameworks (MOF) 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.
Published in Nat. Commun., (2020). DOI: 10.1038/s41467-020-16897-z
The revM11 functional is an improved version of the range-separated parameterization originally used in the M11 functional to obtain a parametrization. It was found to give - by a considerable margin - the best overall performance obtained by any density functional to date when considering all three types of electronic excitations, and at the same time it gives very good predictions across-the-board for ground-state properties.
Published in J. Phys. Chem. A, (2020). DOI: 10.1021/acs.jpca.8b11499
Selected for Virtual Issue on New Tools and Methods in Physical Chemistry Research, June 4. 2020
Postdoctoral associate WooSeok Jeong, Ph.D., graduate student Sam Stoneburner (Stoneburner, Ph.D., graduated in June 2019 and is an assistant professor at Messiah College in Pennsylvania), and Professor Laura Gagliardi designed a project aimed at popularizing complete-active-space self-consistent field by making it easier to select good active spaces. In collaboration with first-year graduate student Daniel King, undergraduate Andrew Walker, visiting undergraduate Ruye Li from Tsinghua University, and Professor Roland Lindh from Uppsala University, they developed a machine learning protocol that performs an automated selection of active spaces for chemical bond dissociation calculations of main group diatomic molecules.
This work was funded by the NMGC
Published in J. Chem. Theory Comput., (2020) DOI: 10.1021/acs.jctc.9b01297.
More energy-efficient separation processes are needed to reduce the energy footprint of the chemical industries, but modeling of complex sorption equilibria is a bottleneck. A deep neural network, called SorbNet, is developed that can predict multi-component adsorption isotherms over a wide range of temperatures and pressures and allows for finding optimal conditions for challenging separations.
Published in Chem. Sci., (2019) DOI: 10.1039/C8SC05340E.
Dr. Xin-Ping Wu, a postdoctoral scholar working with Don Truhlar and Laura Gagliardi, has proposed that metal–organic frameworks (MOFs) containing cerium would also be good photocatalysts; with research supported by the Nanoporous Materials Genome Center.
Published in the J. Am. Chem. Soc., (2018) DOI: 10.1021/jacs.8b03613.
For more than a year, NMGC researchers worked to create a game. Master of Filtering lets players design and test brand new Metal Organic Frameworks (MOFs) within an interactive game center.
Published in the Proceedings of the 9th International Conference on Motion in Games Pages 39-48 (2016). DOI: 10.1145/2994258.2994267.
Researchers at Northwestern University have discovered a way to rapidly identify top candidates for carbon capture — using just 1 percent of the computational effort that was previously required.
Published in Science Advances 14 Oct 2016:Vol. 2, no. 10, e1600909.
Researchers at University of California Berkeley and NIST have determined the underlying mechanistic reasons for the high methane volumetric uptake observed in the Cu3(btc)2 metal organic framework. This joint experimental and computational study highlights the importance of combining these techniques.
Published in J. Am. Chem. Soc. 137, 10816–10825 (2015).
A collaborative work between the University of Minnesota-Twin Cities and the University of California, Berkeley, details the mechanism of oxidation of ethane to ethanol at iron(IV)–oxo sites in magnesium-diluted Fe2(dobdc).
Published in J. Am. Chem. Soc. 137, 5770–5781 (2015).
A team of researchers from Northwestern University and the University of Minnesota have made a significant breakthrough with a new material that is robust and effective at destroying toxic nerve agents.
Published in Nature Materials 14, 512–516 (2015).
Researchers at the University of Minnesota and University of California, Berkeley, make breakthrough discovery into cost-effective and efficient ways to remove carbon dioxide from the atmosphere.
Published in Nature (2015).
A team of researchers led by the University of Minnesota and Rice University has identified potential materials that could improve the production of ethanol and petroleum products.
Published in Nature Communications 6, 5912 (2015).
Newly published research from the collaborative work by the University of California, Berkeley, and the University of Minnesota focuses on the oxidation of ethane to ethanol in a metal-organic framework—a step toward greater energy efficiency.
Published in Nature Chemistry 6, 590–595 (2014).
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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