Accurate molecular crystal modeling for nucler magnetic resonancecrystallography
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AWARD NUMBER
006830-002
FUND NUMBER
21266
STATUS
Closed
AWARD TYPE
3-Grant
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AWARD EXECUTION DATE
6/12/2014
BEGIN DATE
8/1/2014
END DATE
7/31/2017
AWARD AMOUNT
$443,001
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Sponsor Information
SPONSOR AWARD NUMBER
SPONSOR
SPONSOR TYPE
FUNCTION
Organized Research
PROGRAM NAME
Proposal Information
PROPOSAL NUMBER
14030183
PROPOSAL TYPE
Renewal
ACTIVITY TYPE
Basic Research
PI Information
PI
Beran, Gregory J O
PI TITLE
Other
PI DEPTARTMENT
Chemistry
PI COLLEGE/SCHOOL
College of Nat & Agr Sciences
CO PIs
Project Information
ABSTRACT
Gregory Beran at the University of California, Riverside is supported by an award from the Chemical Theory, Models and Computational Methods (CTMC) Program in the Chemistry Division and the Computational and Data-Enabled Science and Engineering Program (CDS&E) to develop state-of-the-art computational strategies for predicting the most stable molecular crystal structures and for coupling these predictions with experiments to determine crystal packing, The efficacy of pharmaceuticals, novel organic electronic materials, and many other crystalline solids depends critically on the manner in which the molecules pack together to form crystals. In the longer term, these studies of pharmaceutical crystal packing will help the formulation of medicinal drugs with improved shelf-life, solubility, and other important properties. Software resulting from this project will be made widely available to the community. The principal investigator also engages in outreach activities to bring awareness of scientific computing to the a broad range of students and to the general public.
To achieve these goals, new electronic structure algorithms that provide more accurate descriptions of intermolecular interactions at lower computational cost are being developed that will enable reliable crystal structure modeling. Improved techniques for predicting nuclear magnetic resonance chemical shifts are being developed to allow mapping between experimentally measured spectra and three-dimensional structural models. High-quality chemical shift predictions are needed to discriminate among different crystal packing forms. These techniques are being integrated into a workflow that combines crystal structure prediction, nuclear magnetic resonance spectrum simulations, and experimental measurements to identify the structures for several unknown crystal forms of two important pharmaceuticals. The new methods are incorporated into software which will be released to under open-source licensing terms. The code will be integrated into the Q-CHEM package, a commercial code with a very large user base.(Abstract from NSF)
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