With this award, the Chemical Catalysis Program of the NSF Division of Chemistry is supporting the research of Professor Scott E. Denmark of the Department of Chemistry at the University of Illinois at Urbana-Champaign. Professor Denmark is developing a new paradigm for the development of catalysts that combines the creative power of "diversity-oriented synthesis" with the computational power of informatics. Under the data-driven discovery science in Chemistry (D3SC) initiative, the foundation of this program is the invention and implementation of high-resolution descriptors that are able to accurately reflect the chemical properties of molecules in a form that is readable by a computer. The current state of the art is insufficient to provide the level of granularity needed to assure that the properties of computer generated libraries of hypothetical catalysts are accurate reflections of the true character of the molecules. Most importantly, however, the development of a truly general, computationally guided workflow for the optimization of molecular function can impact the entire spectrum of chemical properties from optimizing the performance of functional materials (sensors, LEDs, adhesives, energy storage systems, etc.) to the discovery and optimization of catalysts for industrially relevant chemical processes.
The goals of this proposal are summarized in the basic components of a discovery oriented program that combines computational analysis with experimentation, namely: (1) in-silico generation of a massive library of hypothetical catalyst structures based on a given scaffold followed by calculation of descriptors of each library member, (2) diversity analysis to generate representative "training set", (3) synthesis of training set, (4) evaluation of the training set in a given reaction, (5) development and validation of a mathematical model that correlates empirical output with molecular properties (6) application of that model to the virtual library of catalysts, (7) synthesis and evaluation of best predicted catalysts, and (8) repeat steps 4-8 until desired output is achieved. Great potential exists for 3D-Quantitative Structure Selectivity Relationship (3D-QSSR) modeling to impact asymmetric catalysis, not only by identifying high performance catalyst structures, but also by providing a new framework to elucidate the structural features that govern the activity and enantioselectivity of catalysts for any given chemical transformation. Most importantly, this framework should be universally applicable to the optimization of any kind of molecular property or function. These activities are ideal for the intellectual and practical training of graduate students and postdoctoral coworkers. The interplay of theory and experiment is the essence of the scientific method. Students are presented with hypotheses for the outcome of planned experiments and they must learn to collect and interpret data to substantiate or eliminate the hypothesis. They also become expert synthetic chemists, proficient scientific programmers, and competent in computational chemistry, data-science, and machine learning. The unifying theme of this activity is the development of a computationally guided workflow that leads to the creation of universally applicable training sets of molecules that can be used to optimize myriad chemical reactions.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
