predictive hierarchical modeling of chemical separations and transformations in functional nanoporous materials: synergy of electronic structure theory, molecular simulation, machine learning, and experiment
February 18, 2020 | An NMGC paper “Energy-Based Descriptors to Rapidly Predict Hydrogen Storage in Metal–Organic Frameworks” has been awarded the Senior Moulton Medal for most meritorious paper published by the Institution in 2019-2020.
February 4, 2020 | Introduced in 1979, the Kuwait Prize recognizes the lifetime achievements of scientists of Arab descent across the globe.
18 November, 2019 | Christopher Cramer, Omar Farha, Donald Truhlar, Joseph Hupp, Jeff Long and Randy Snurr have been included in the Clarivate Analytics Highly Cited Researcher List.
Researchers developed a machine learning protocol that performs an automated selection of active spaces for chemical bond dissociation calculations of main group diatomic molecules.
A deep neural network called SorbNET has been developed that can predict multi-component adsorption isotherms over a wide range of temperatures and pressures.
Dr. Xin-Ping Wu, a postdoctoral scholar working with Donald Truhlar and Laura Gagliardi, has proposed that Metal Organic Frameworks (MOFs) containing cerium would be good photocatalysts.
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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