GEORGIA INSTITUTE OF TECHNOLOGY

Under the provisions of the regulations for the degree

DOCTOR OF PHILOSOPHY

on Wednesday, April 28, 2021

1:00 PM EST

via

Blue Jeans Video Conferencing

https://bluejeans.com/549327469

will be held the

DISSERTATION  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. Sundaresan 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 challenging task.

My work involves the use of data-driven 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 a large chemical space, followed by the actual building of the machine learning models. These predictive models are then used to rapidly screen a large candidate space of 13,388 synthesizable polymers to identify new polymers and subsequent chemical rules for promising and reliable SPEs. Beyond screening for potential SPEs, we also develop models to assist in their synthesis, specifically in identifying solvent and non-solvents suitable for electrolyte synthesis and processing. The workflow presented in this thesis demonstrates the capabilities of combining computational and experimental data with data-driven methods to accelerate the design and discovery of high-performing and safe solid polymer electrolytes.