predictive hierarchical modeling of chemical separations and transformations in functional nanoporous materials: synergy of electronic structure theory, molecular simulation, machine learning, and experiment
April 28, 2021 | Yangzhesheng (Andrew) Sun (Siepmann Group) has been awarded a Dissertation Fellowship.
April 26, 2021 | Laura Gagliardi has been elected into the National Academy of Sciences (NAS). Members are elected by their peers and this honor recognizes her distinguished and continuing achievements in original research.
April 22, 2021 | Joseph Hupp has been elected a Fellow of the American Academy of Arts and Sciences (AAAS). The Academy honors people making preeminent contributions to their fields and the world. Members are leaders in the academic disciplines, the arts, business, and public affairs.
The work of NMGC researchers in the groups of Coray Colina (University of Florida) and David Sholl (Georgia Tech), which appeared in Nature Publishing Journal Computational Materials, provides an extensive dataset of over 240,000 structural conformations of amorphous polymeric materials, adsorption isotherms that account for polymer flexibility, and binary selectivities of 4,140 polymer-mixture combinations, with almost all of them unavailable from experimental works.
The groups of NMGC researchers Randall Snurr (Northwestern University), Laura Gagliardi (University of Chicago), and Alán Aspuru-Guzik (University of Toronto) introduce the Quantum MOF (QMOF) database, a publicly available database of computed quantum-chemical properties for more than 14,000 experimentally synthesized MOFs. In a recent study, the authors demonstrate how machine learning models trained on the QMOF database can be used to rapidly discover MOFs with targeted electronic structure properties, using the prediction of theoretically computed band gaps as a representative example.
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.
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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