Quantitative and Qualitative Tools for Predicting the Products and Mechanisms of Chemical Reactions

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Chemists solve problems by making molecules and materials with useful properties. Sometimes a chemical problem can be solved by finding an inexpensive and environmentally friendly way to manufacture a known substance. Computational chemistry software allows chemists to explore possible synthetic pathways before embarking on the time-consuming quest to make a molecule. Sometimes solving a chemical problem requires creating an entirely new substance. It is impossible to systematically test all possible substances, so chemists use qualitative theoretical rules and quantitative computations to navigate through the astronomical number of possibilities until they find a substance with desirable properties. ******I develop theoretical models and computational software for predicting chemical reaction pathways, describing chemical phenomena, and designing molecules with desirable properties. At the most fundamental level, I develop new computational methods to describe how electrons bind atoms into molecules. In conventional approaches to this problem, electrons are assumed to move quasi-independently, so that a single electron configuration can be clearly identified. This approximation is unsuitable for exploring chemical reactivity because it is unreliable for strained molecular geometries (chemical transition states, stretched bonds, etc.). Because traditional multiconfiguration methods are computationally expensive and relatively difficult to use, my group is developing an alternative approach, which shares some of the features (computational speed, ease-of-use) with single-configuration methods, but which is not biased towards any specific electron configuration. Our method will allow us to reliably predict the energy of molecular structures and, thereby, predict how chemical reactions occur. My group also develops conceptual tools that predict the most reactive parts of a molecule and allow us to understand why some reactions occur readily, while others do not. These insights lead to intuitive rules that chemists can use to guide the search for new phenomena. These tools can also be used by machine-learning methods, where a computer (rather than a human chemist) gets data about the features and properties of known molecules, discovers rules for organizing this data, and uses these rules to predict new molecules with desirable properties. These methods are disseminated as free and open-source software, usually as part of the HORTON package.