Novel Mechanical Functionality in Nano-Architectured Ferroelectrics via Rational Design of Free Energy Landscapes

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Ferroelectrics are important materials used for sensing and telecommunication. Mechanical motion can be induced in ferroelectrics by applying electric fields, which manipulate the orientation of nanoscale clusters of electric dipoles, known as domains. Advances in the growth of thin films now allow artificial ferroelectric materials to be created by combining layers that differ in chemical composition or domains into a single structure, one atomic layer at a time. In comparison to traditional ferroelectric materials, artificial systems offer new ways to manipulate domains and thereby mechanical behavior. This award aims to understand how “smart” mechanical behaviors, such as the ability to induce mechanical motion with light or the ability to tune mechanical resonance with light or stress, can be engineered in artificial ferroelectrics. This research will integrate advanced computational techniques with the fabrication and characterization of microscale mechanical devices based on artificial ferroelectrics. The project will also provide opportunities to educate and train graduate and undergraduate students in the cross-disciplinary areas of materials science, physics, device engineering, and data science. Outreach and dissemination efforts will include educational modules for local middle and high school students, as well as displays for Dallas and Fort Worth area science museums. Ferroelectrics are key materials in microelectromechanical systems (MEMS). Recent advances in thin-film epitaxy have enabled artificial ferroelectric systems to be created by combining compositionally diverse layers into monolithic heterostructures. In contrast to systems of homogeneous composition, artificial systems offer unprecedented pathways to tune the free energy landscape, and thereby mechanical response. This proposal aims to elucidate how free energy landscapes, and thereby mechanical functionality, can be rationally engineered in artificial ferroelectric heterostructures. Of particular interest are materials poised near instabilities in the free energy landscape that may lead to dramatic mechanical response to perturbations. Such materials could also exhibit smart functional characteristics, including light-induced mechanical actuation and mechanical resonance that can be tuned on-demand. The approach involves phase-field modelling of ferroelectric heterostructures comprised of perovskite ABO3 (A = Sr, Ba; B = Ti, Zr) layers of varying composition. Machine learning on a database generated by high-throughput phase-field simulations will identify and correlate key material characteristics (or “fingerprints”) of heterostructures to mechanical response. Heterostructures will be experimentally realized through epitaxial growth on silicon using oxide molecular beam epitaxy. Microbeam resonators and cantilevers will be created from these heterostructures, and electromechanical behavior will be probed under perturbation of mechanical stress and optical excitation to form a close-loop study.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.