CAREER: CDS&E: Predictive Discovery of Complex Reaction Mechanisms

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Paul Zimmerman at the University of Michigan is supported by the Chemical Theory, Models and Computational Methods Program and the Computational and Data-Enabled Science and Engineering (CDS&E) Program to develop new tools to predict the outcome of chemical reactions. Computational chemistry has a long history of using the principles of quantum mechanics to create tools which provide detailed, accurate explanations for a wide variety of chemical processes. Many of these tools have reached sufficient accuracy that they can be applied to discover chemical reactions without requiring prior insight from experiment. Unfortunately, the high computational cost of these methods has prevented their broad use in predicting chemical reactivity, especially in cases where the chemistry is highly complex or poorly understood. This grant supports the development of fully computational, low-cost and highly accurate methods that are able to predict the outcome of chemical reactions starting only from the feedstock molecules. The application focus of these methods is catalytic reactions, providing a highly useful tool for research into chemistries that can be applied at an industrial level to create high-value chemical products. These tools are available to the wider computational chemistry community, enabling maximized impact of the developments to a great number of problems in chemical reactivity. Due to the diversity of potential applications for this research, the students involved in this project gain not only algorithm development abilities, but also fundamental insights into processes governing molecular behavior, cutting-edge computational research experience, and problem-solving abilities that are needed to address the challenges of the 21st century. The proposed methods are transformed into educational strategies that merge introductory laboratory exercises with real-world research, starting with a pilot study in honors organic chemistry. The research program builds upon work by the Zimmerman group that shows chemical reactions can be described in terms of a small number of localized, anharmonic reaction coordinates. These reaction coordinates consist of interatomic distances, angles, and torsions, and are a reliable, transferable basis for describing atomic motion. By employing advanced single-ended chain-of-states optimization algorithms which search along these coordinates for plausible chemical reactions, this research methodology can efficiently and reliably predict reactive events without guidance from chemical intuition. The main objective of ongoing work is to expand this method to cover transition metal elements, enable efficient conformation searches in systems with floppy degrees of freedom, and develop machine learning algorithms to automatically process chemical data and provide great enhancements in computational efficiency. In sum, these new reaction search techniques enable predictive reaction discovery in a wide variety of large, highly complicated chemical systems where chemical knowledge alone is not yet sufficient to make accurate predictions. These methods are being applied to challenging cases in catalysis where uncharacterized side reactions are severe impediments to efficient product formation. Ongoing discovery of these undesired reaction pathways lead to the chemical insight required to improve reaction selectivity and catalyst stability.These tools allow beginning chemistry students to hypothesize and evaluate reactions in silico, resulting in a means for students to perform research at an early stage of their studies. Such pedagogical tools are distributed to educators outside of the University of Michigan to maximize their impact.