David Yaron,
Associate Professor
Chemistry Department, Carnegie Mellon University
Electronic structure of chemical systems: A playground for computational thinking
November 13, 3305 Newell-Simon Hall, 4:00 p.m.
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Abstract

In 1929, the discovery of quantum mechanics prompted physicist Paul Dirac to say ".. the underlying physical laws necessary for the mathematical theory of a large part of physics and the whole of chemistry are thus completely known". The analytic power of differential equations were indeed in place, but it was not until the end of the 20 th century that accurate solutions would be routinely available, and even then, only for relatively small molecules. While much current effort is expended towards reducing the computer time required for such analytical approaches, alternative forms of computational thinking are possible and may get us more rapidly to the point where we can study the large systems of relevance in biology and nanotechnology.

We are exploring alternative forms of computational thinking that are data based, as opposed to purely analytically based. These alternative forms of thinking take advantage of existing analytical algorithms to generate a large and rich data base on chemical functional groups in different environments. We then investigate (mine) these data to develop a model that that can describe electron motion in large systems. The ability to generate clean data as needed affords considerable freedom, limited only by the imagination and knowledge of the investigator.

Bio

Dr. Yaron received his Ph. D. in chemical physics from Harvard University in 1990. He was a research fellow at MIT for two years before joining the Department of Chemistry at Carnegie Mellon in 1992. His thesis was in experimental physical chemistry and his research fellowship and current research are in theoretical and computational chemistry. He has won a number of awards and fellowships including The Camile and Henry Dreyfus Foundation Teacher-Scholar Award (2000). His chemical research interests include nanotechnology, organic semiconductors, and the application of machine learning to chemical modeling. His educational research focuses on the development and assessment of interactive learning environments for chemical education.