D3SC: In Silico Design of Molecular Catalysts for C-H Functionalization via Machine Learning Algorithms

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Can we teach a machine chemical intuition so that the machine can help us discover new molecules and materials with useful properties? Can doing so accelerate these discoveries? These are questions that Dr. Vogiatzis of the University of Tennessee is addressing. To achieve these goals, he is applying statistical models that can learn new complex mathematical functions. These functions have the power to connect meaningful chemical information with molecular structure by looking for patterns that contribute to important and desirable properties. This chemically-driven machine learning (CDML) computational approach is a promising technique for a large variety of environmental, biological, and energy-related problems. To demonstrate the strength and applicability of CDML, Dr. Vogiatzis and his research group are examining the chemical properties of iron species that mimic the action of iron-containing enzymes. Dr. Vogiatzis is providing interdisciplinary research opportunities to students, including those from underrepresented groups. In turn, they are learning about data science and machine learning methodologies, an important tool for the development of tomorrow's technologies. The computational tools developed in Dr. Vogiatizis' research group are being provided free to other researchers interested in machine learning and chemistry.With funding from the Chemical Catalysis Program and the D3SC (Data Driven Discovery Science in Chemistry) initiative of the Chemistry Division, Dr. Vogiatzis of the University of Tennessee is developing computational tools for efficient high-throughput computational screening of large libraries of molecular complexes. The long-term target is the design of the next generation of catalysts for efficient C-H functionalization via quantum chemistry and machine learning. His research group is currently working on an integrated computational protocol that examines one class of reactive sites for C-H activation, but the proposed methodology is transferable to other chemical procedures as well. The biomimetic catalytic site that is currently examined is the Fe(IV)-oxo intermediate, active site of heme and non-heme enzymes, and is chosen due to the vast literature that can guide the development of the computational model. Dr. Vogiatzis is engaging undergraduate and graduate students from underrepresented groups in his research. He is also engaging students from East and Central Tennessee about his scientific interests. In addition, the development of free, open-source software is of high importance for the scientific community and the advance of the science. The computational tools that are developed with funding from the Chemical Catalysis Program are being provided free-of-charge to other researchers interested in machine learning and chemistry.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.