PhD Proposal by Shruti Venkatram

Event Details
  • Date/Time:
    • Monday April 20, 2020
      1:00 pm - 3:00 pm
  • Location: REMOTE
  • Phone:
  • URL: BlueJeans
  • Email:
  • Fee(s):
    N/A
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Summaries

Summary Sentence: Machine Learning Based Models for the Design of Solid Polymer Electrolytes

Full Summary: No summary paragraph submitted.

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.

 

Additional Information

In Campus Calendar
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Groups

Graduate Studies

Invited Audience
Public, Graduate students, Undergraduate students
Categories
Other/Miscellaneous
Keywords
Phd proposal
Status
  • Created By: Tatianna Richardson
  • Workflow Status: Published
  • Created On: Apr 6, 2020 - 11:19am
  • Last Updated: Apr 6, 2020 - 11:19am