Rational, computational design is a promising pathway to the development of novel catalysts, enzymes, and materials, but the effectiveness of this design strategy is dependent upon the predictive accuracy and computational cost of the methods employed.  Density-functional theory(DFT) is a widely-employed approach for determining the electronic structure of molecules and solids, but standard DFT suffers from a critical error known as self-interaction that yields erroneous descriptions of geometry and energetics. By augmenting standard DFT with a system-dependent Hubbard term, my methods achieve a level of accuracy that provides insights into the tethered catalysis activity of a halogenase relevant for natural product synthesis, new strategies for carbon capture catalyst design, and predictive descriptions of chemisorption on surfaces. Additionally, my recent work in accelerated quantum chemistry on novel architectures enables the determination of the electronic structure for systems thousands of atoms in size, paving the way for new enzyme and drug design.

Predictive and fast: New tools for first-principles catalyst and enzyme design

244th National Meeting (2012)

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