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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Summary Sentence: "Prediction and Design in Chemical Evolution" - Martha Grover, PhD - Georgia Tech
Full Summary: The Petit Institute Breakfast Club seminar series was started with the spirit of the Institute's interdisciplinary mission in mind and started to feature local Petit Institute faculty member's research in a seminar format. Faculty are often asked to speak at other universities and conferences, but rarely present at their home institution, this seminar series is an attempt to close that gap. The Petit Institute Breakfast Club is open to anyone in the bio-community.
Breakfast Club Seminar Series"Prediction and Design in Chemical Evolution"
Martha Grover, PhD
Associate Professor of Chemical & Biomolecular Engineering
Duncan Mellichamp Faculty Fellow
Georgia Tech
Discrete atoms and molecules interact to form macromolecules and even larger mesoscale assemblies, ultimately yielding macroscopic structures and properties. A quantitative relationship between the nanoscale discrete interactions and the macroscale properties is required to design, optimize, and control such systems; yet in many applications, predictive models do not exist or are computationally intractable.
The Grover group is dedicated to the development of tractable and practical approaches for the engineering of macroscale behavior via explicit consideration of molecular and atomic scale interactions. We focus on applications involving the kinetics of self-assembly, specific those in which methods from non-equilibrium statistical mechanics do not provide closed form solutions. General approaches employed include stochastic modeling, model reduction, machine learning, experimental design, robust parameter design, and estimation.
