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<b style="font-family: "Times New Roman", serif; font-size: 12pt; color: inherit; font-style: inherit; font-variant-ligatures: inherit; font-variant-caps: inherit; background-color: ;"><span style="font-size:14.0pt; color:#0D0D0D">NAME</span></b><b style="font-family: "Times New Roman", serif; font-size: 12pt; color: inherit; font-style: inherit; font-variant-ligatures: inherit; font-variant-caps: inherit; background-color: ;"><span style="font-size:14.0pt; color:#005A58">
</span></b><span style="font-family: "Times New Roman", serif; font-size: 14pt; color: rgb(13, 13, 13);">Karun Kumar Rao</span><br>
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<b><span style="font-size:14.0pt; color:black">COMMITTEE CO-CHAIRS: </span></b><span style="font-size:14.0pt; color:black">Dr. Lars Grabow and Dr. Yan Yao</span><span style="font-size:14.0pt"></span></p>
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<b><span style="font-size:14.0pt; color:black">DATE: </span></b><span style="font-size:14.0pt; color:black">Tuesday, December 01, 2020</span><span style="font-size:14.0pt"></span></p>
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<b><span style="font-size:14.0pt; color:black">TIME: </span></b><span style="font-size:14.0pt; color:black">2:00 PM (CST)</span><span style="font-size:14.0pt"></span></p>
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<b><span style="font-size:14.0pt; color:black">LOCATION:</span></b><span style="font-size:14.0pt; color:black"> Microsoft Teams </span><span style="font-family:"Tahoma",sans-serif"><a href="https://urldefense.com/v3/__https:/teams.microsoft.com/l/meetup-join/19*3ameeting_ZWI1NmZjYmYtNDE5Ni00MGI1LTk0MGYtYWUwYTYxZDIxOTcz*40thread.v2/0?context=*7b*22Tid*22*3a*22170bbabd-a2f0-4c90-ad4b-0e8f0f0c4259*22*2c*22Oid*22*3a*2234b1c3d8-bc56-4e62-ad8b-3b4c7c61052f*22*7d__;JSUlJSUlJSUlJSUlJSUl!!LkSTlj0I!QSlB0pFA3QCw9DTUpKiF1PDWJKe2tlLSUGw4LvSmS2RLahBoPtmpx7dRnOWssFd5jKxQ$" target="_blank"><span style="font-size:13.5pt; font-family:"Segoe UI Semibold",sans-serif; color:#6264A7">Join
Microsoft Teams Meeting</span></a><span style="color:#005A58"> </span></span></p>
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<b><span style="font-size:14.0pt; color:black">TITLE: </span></b><b><span style="font-size:14.0pt; color:#005A58"></span></b></p>
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<b><span style="font-size:13.5pt">Machine Learning Guided Discovery of Advanced Functional Energy Materials</span></b></p>
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<b><span style="font-size:13.5pt">Abstract</span></b></p>
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All solid-state batteries provide many safety advantages over traditional lithium-ion batteries by replacing the combustible organic liquid electrolyte with a ceramic solid-state electrolyte (SSE). However, the ionic conductivity in these SSEs is often
several orders of magnitude lower than in their liquid counterparts. First-principles (i.e., with density functional theory, or DFT) molecular dynamics (MD) is an established approach to calculate and study ionic conductivity, but is limited in the number
and type of materials that can be simulated due to the high computational cost. To this end, we leverage advanced machine learning (ML) algorithms to more efficiently calculate ionic conductivity and optimize material composition.<br>
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To accelerate the calculation of forces and energies in MD, we train an artificial neural network force field, which scales linearly and enables the calculation of ionic conductivity at experimentally relevant scales. However, predicting a material’s ionic
conductivity directly from its crystal structure is limited by data availability, incomplete material descriptors, and the inability of models to extrapolate to physically relevant conditions or new materials. By using a partial least squares algorithm with
valence electronic density as an input, we identify and quantify the BCC anion substructure and interstitial density as effective physical descriptors. Additional machine learning models trained to predict the lithium probability density circumvent training
limitations and highlight the importance of property representation in model performance. Our novel 3d material segmentation network provides both quantitative and qualitative insight on the topology of diffusion pathways to accelerate SSE design. Using these
models, we identified several new promising classes of solid-state electrolyte candidates to have conductivities greater than 16 mS/cm as verified by DFT-MD simulations.<br>
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The methods and outcomes described in this thesis generalize to other solid-state systems including single atom alloys for heterogeneous catalysis. The proposed combinations of first principles simulation data with ML models will greatly accelerate the
rate of materials design and discovery, and can automate calculating structure-property relationships for other applications with high accuracy and without sacrificing interpretability.</p>
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