Graduation Semester and Year
Summer 2026
Language
English
Document Type
Thesis
Degree Name
Doctor of Philosophy in Materials Science and Engineering
Department
Materials Science and Engineering
First Advisor
Dr. Ye Cao
Abstract
Ferroelectrics underpin a broad spectrum of technological applications due to its switchable ferroelectric polarization and the associated electro-mechanical responses under electrical, optical, thermal, and mechanical stimuli. Recent advancement in membrane technology offers new opportunities to tune ferroelectric polarizations via mechanical strains at relatively large magnitude and scale. However, its influence on the tunability of mechanical responses of the membrane remains underexplored. Herein, we developed a phase-field model for free-standing Ba1-xSrxTiO3 ferroelectric membranes with stress-free boundary conditions on top/bottom surfaces and achieved strain-induced nonvolatile ferroelectric domain switching in the membrane. It is discovered that a tensile/compressive strain favors in-plane orthorhombic/out-of-plane tetragonal domain structures in BaTiO3, which are stable after the strain is removed. This results in differences in the elastic modulus of BaTiO3 membrane under different domain states, i.e., a large tunability of the elastic modulus. By controlling the temperature and Sr composition of the membrane, we found that the highest elastic tunability of ~54% can be achieved at the paraelectric-ferroelectric (tetragonal) phase boundaries due to the energy degeneracy of the two phases that facilitates ferroelectric-to-paraelectric phase transition. Local enhancement of elastic tunability is also realized at the phase boundaries between rhombohedral, orthorhombic, and tetragonal phases in the ferroelectric states. To further accelerate materials design, a machine learning framework trained on the high-throughput phase-field dataset is integrated to establish an inverse mapping from domain microstructures and elastic modulus to the underlying temperature and composition. This approach enables a faster and more efficient way to identify optimal material conditions. Overall, this study provides insight into strain-driven nonvolatile domain switching and mechanical tunability in ferroelectric membranes and offers a combined physics-based and data-driven approach for designing materials with tailored properties for applications such as tunable devices and nonvolatile memory.
Keywords
Ferroelectrics, phase-field modeling, finite element analysis, machine learning, barium titanate, domain switching, ferroelectric membrane, nonvolatile memories, tunable elastic modulus, ferroelectric oxide
Disciplines
Other Materials Science and Engineering
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Ahmad, Laveeza, "Strain-induced nonvolatile domain switching and tunable elastic modulus in Ba1-xSrxTiO3 membrane by phase-field simulation." (2026). Material Science and Engineering Dissertations. 2.
https://mavmatrix.uta.edu/materialscieng_dissertations2/2