THE SCHOOL OF MATERIALS SCIENCE AND ENGINEERING
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
Blue Jeans Video Conferencing
will be held the
"Machine Learning Based Models for the Design of Solid Polymer Electrolytes"
Prof. Rampi Ramprasad, Advisor, MSE
Prof. Blair Brettmann, ChBE/MSE
Prof. Sundaresan Jayaraman, MSE
Prof. Seung Soon Jang, MSE
Prof. Roshan Joseph, ISyE
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.