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Title: Multivariate Machine Learning Enabled Study of the Local Electromechanical Response of Relaxor-Ferroelectric Solid Solutions
Committee:
Dr. Bassiri-Gharb, Advisor
Dr. Khan, Co-Advisor
Dr. Degertekin, Chair
Dr. Sulcheck
Abstract:
The objective of the proposed research is to understand the effects of composition, the various heterogeneities, phase transitions, and extrinsic effects on the local electromechanical response of PMN-xPT. This will be achieved via multivariate in-situ characterizations. Additionally, this proposal seeks to engineer novel means of analysis and characterization that enable new studies and facilitate more meaningful interpretations. Specifically, a new wide band PFM probing technique with continuous frequency tracking is developed, enabling local characterizations previously inaccessible via PFM. Furthermore, a novel machine learning approach to analysis of multidimensional functional characterizations is presented. By imposing physical and/or chemical boundary conditions onto the data set, this approach correlation of results across multiple measurement parameters. Both of the above techniques are used to study the evolution of the nanoscale electromechanical response of relaxor-ferroelectric solid solution single crystals. Specifically, the time-dependent local electromechanical response is investigated after removal of a perturbing electric field. The observed local behavior is characterized through machine learning approaches as a function of composition, domain size/density, and electric field polarity. Preliminary results identify a persistent domain glass state across the phase diagram, regardless of the domain size or polarity of the probing electric field. Furthermore, a poling-like behavior and a relaxation-like behavior are observed across the phase diagram. Subsequent studies will investigate the spatial evolution of and compare in-field poling and out-of-field relaxation behaviors.
