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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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THE SCHOOL OF MATERIALS SCIENCE AND ENGINEERING
GEORGIA INSTITUTE OF TECHNOLOGY
Under the provisions of the regulations for the degree
DOCTOR OF PHILOSOPHY
on Friday, October 8, 2021
11:00 AM
via
BlueJeans Video Conferencing
https://bluejeans.com/526281391/5916
will be held the
DISSERTATION PROPOSAL DEFENSE
for
Kerisha Nicole Williams
"Machine Learning for Piezoresponse Force Microscopy"
Committee Members:
Prof. Nazanin Bassiri-Gharb, Advisor, MSE/ME
Prof. Lauren Garten, MSE
Prof. Asif Kahn, ECE
Prof. Yao Xie, ISYE
Prof. Eric Vogel, MSE
Abstract:
Piezoresponse force microscopy (PFM) probes the nanoscale electromechanical response of materials through measurement of the sample surface displacement upon application of electric field through a conductive cantilever tip. However, in addition to piezoelectric coupling, non-piezoelectric phenomena such as electrostatic forces arising from charge injection and electro-chemo-mechanical force arising from ionic and defect motion can also contribute to the observed response. Hence, analysis of the observed response is hindered in physical interpretation. Moreover, both piezoelectric and non-piezoelectric contributors are modulated by changes to the materials surface and/or the local environment, further limiting physical insights into the underlying materials response. While such limitations might be marginal in epitaxial ferroelectric ultra-thin films samples often used in PFM studies, no material is a priori impervious to them. Machine Learning techniques have been increasingly used in the analysis of PFM data to overcome some of these interpretation challenges. Specifically, dimensional stacking (concatenation of multiple datasets) can be used to first encode physical constraints into multi-dimensional datasets before separation of co-contributing behaviors via (unsupervised) dimensional reduction techniques. This work proposes to use PFM combined with machine learning to separate and quantify the chemical and physical phenomena contributing to nanoscale electromechanical behavior of ferroelectric perovskites observed, as characterized by resonant PFM.
