Sharon Glotzer

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Sharon Glotzer, PhD, is the Anthony C. Lembke Department Chair of Chemical Engineering, the John Werner Cahn Distinguished University Professor of Engineering, and the Stuart W. Churchill Collegiate Professor of Chemical Engineering, in the College of Engineering, at the University of Michigan, Ann Arbor.

Prof. Glotzer and her group are focused on the new revolution in nano-science, engineering and technology is being driven by our ability to manipulate matter at the molecular, nanoparticle, and colloidal level to create “designer” structures. The Glotzer group uses computer simulation to discover the fundamental principles of how nanoscale systems of building blocks self-assemble, and to discover how to control the assembly process to engineer new materials. By mimicking biological assembly, we are exploring ways to nano-engineer materials that are self-assembling, self-sensing, and self-regulating.

The group is developing theory and molecular simulation tools to understand these materials, and elucidate the nature of supercooled liquids, glasses, and crystallization.

The Glotzer group develops in-house, open-source software for simulation, data analysis, and more. We invite the scientific community to learn more and utilize our software.

Paul Zimmerman

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Paul Zimmerman, PhD, is Assistant Professor of Chemistry, College of Literature, Science, and the Arts, at the University of Michigan, Ann Arbor.

The Zimmerman research group is a pioneer in computational chemical reaction discovery, and has developed algorithms which map out complex multi-component, multi-elementary step reaction mechanisms without reliance on prior chemical knowledge. These methods, being low computational cost compared to other quantum chemical techniques, provide a large amount of detailed and accurate chemical information to be processed and exploited. These data present an opportunity for application of statistical methods, specifically from machine learning, to determine the key chemical features that enable chemical reactions to occur. Machine learning not only provides a detailed analysis of how chemical processes work, but also provides a rapid, predictive method to determine the rate and selectivity of novel chemical reaction sequences. Ongoing work in the Zimmerman group is taking advantage of tools such as the Kernel Ridge and logistic regressions, k-nearest neighbors, and genetic algorithms to transform chemical reaction data into predictive tools applicable to many types of chemistry.

Successful k-NN organization of reaction data involving nearly 700 unique chemical reactions.

Successful k-NN organization of reaction data involving nearly 700 unique chemical reactions.