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  <title><![CDATA[PhD Proposal by Shruti Venkatram]]></title>
  <body><![CDATA[<p><strong>THE SCHOOL OF MATERIALS SCIENCE AND ENGINEERING</strong></p>

<p>&nbsp;</p>

<p><strong>GEORGIA INSTITUTE OF TECHNOLOGY</strong></p>

<p>&nbsp;</p>

<p><strong>Under the provisions of the regulations for the degree</strong><br />
<br />
<strong>DOCTOR OF PHILOSOPHY</strong><br />
<br />
<strong>on Monday, April 20, 2020</strong></p>

<p><strong>1:00 PM</strong></p>

<p><strong>via</strong></p>

<p><strong>Blue Jeans Video Conferencing</strong></p>

<p><strong><a href="https://bluejeans.com/418265627" target="_blank">https://bluejeans.com/418265627</a></strong></p>

<p>&nbsp;</p>

<p><strong>will be held the</strong></p>

<p>&nbsp;</p>

<p><strong>DISSERTATION&nbsp; PROPOSAL DEFENSE</strong><br />
<br />
<strong>for</strong></p>

<p>&nbsp;</p>

<p><strong>Shruti Venkatram</strong></p>

<p>&nbsp;</p>

<p><strong>&quot;Machine Learning Based Models for the Design of Solid Polymer Electrolytes&quot;</strong></p>

<p>&nbsp;</p>

<p><strong>Committee Members:</strong></p>

<p>&nbsp;</p>

<p><strong>Prof. Rampi Ramprasad, Advisor, MSE</strong></p>

<p><strong>Prof. Blair Brettmann, ChBE/MSE</strong></p>

<p><strong>Prof. Sunderasan Jayaraman, MSE</strong></p>

<p><strong>Prof. Seung Soon Jang, MSE</strong></p>

<p><strong>Prof. Roshan Joseph, ISyE</strong></p>

<p>&nbsp;</p>

<p><strong>Abstract:</strong></p>

<p>&nbsp;</p>

<p>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.</p>

<p>&nbsp;</p>
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