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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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THE SCHOOL OF MATERIALS SCIENCE AND ENGINEERING
GEORGIA INSTITUTE OF TECHNOLOGY
Under the provisions of the regulations for the degree
DOCTOR OF PHILOSOPHY
on Monday, April 20, 2020
1:00 PM
via
Blue Jeans Video Conferencing
https://bluejeans.com/418265627
will be held the
DISSERTATION PROPOSAL DEFENSE
for
Shruti Venkatram
"Machine Learning Based Models for the Design of Solid Polymer Electrolytes"
Committee Members:
Prof. Rampi Ramprasad, Advisor, MSE
Prof. Blair Brettmann, ChBE/MSE
Prof. Sunderasan Jayaraman, MSE
Prof. Seung Soon Jang, MSE
Prof. Roshan Joseph, ISyE
Abstract:
With the prolific popularization and development of lithium-ion batteries, safety issues associated with the use of flammable organic electrolytes have increasingly garnered more attention. A promising alternative to organic liquid electrolytes are solid polymer electrolytes (SPEs) which demonstrate low flammability, good processability and no leakage issues. However, presently known SPE candidates fall short of the required performance requirements, which are reliant on meeting a variety of material property requirements, such as polymer amorphicity, high ionic conductivities at room temperature, large electrochemical stability windows (4V vs Li+/Li), high Li ion transference, moderate tensile strength and thermal stability. Parsing the expansive polymer chemical space for viable SPE candidates which meet the aforementioned criteria is a non-trivial task. My work involves the use of data-driven methods and machine learning methods to build predictive models of a variety of polymer properties relevant for the SPE application. Development of such predictive models require the collection and curation of the requisite data (from computational and experimental sources) for polymers spanning large enough chemical space, followed by the actual building of the machine learning models. These predictive models will then be used to rapidly screen a large candidate space of polymers in an attempt to identify new polymer formulations that may be promising SPEs for safer and more reliable Li-ion batteries.
