An atomistically informed phase field model for ferroelectric materials

This Ph.D. thesis introduces an advanced modeling approach for perovskite-based ferroelectric materials through an atomistically informed phase field (PF) model. Fer-roelectric materials, characterized by spontaneous electric polarization, exhibit unique electromechanical coupling behavior, making them promising candidates for a myriad of applications, including sensors, actuators, and energy storage devices. However, the complexity of these materials necessitates a comprehensive understanding across mul-tiple scales to unlock their full potential. At the forefront of this research is the estab-lishment of a numerical solver employing a Fourier spectral method, which effectively addresses the copuled constitutive and balance equations inherent in the PF modeling of ferroelectrics. This solver serves as a crucial tool for simulating and understanding the complex electromechanical phenomena at play in these materials, and it is employed for all PF simulations throughout the research, ensuring efficient and precise computation. In conjunction with the numerical solver, a novel statistical method is introduced for extracting thermal activation parameters from Molecular Dynamics (MD) data. Specifically, we focus on calculating the activation parameters for BaTiO3 that govern polarization switching at coercive fields, obtained using MD simulations, with a partic-ular emphasis on the activation energy and activation volume. Activation energies and volumes are determined not only for perfect, defect-free crystals but also for crystals with monovacancies and divacancies. Collaborative efforts with Japanese counterparts from Nagoya Institute of Technology yielded MD data that serves as a foundational reference for this research. Leveraging these thermal activation parameters, a robust parametrization of PF simulations is achieved, enhancing the accuracy and reliability of the modeling framework. Overall, the research presented in this thesis not only contributes to the development of efficient numerical tools for simulating ferroelectrics but also establishes a robust framework for incorporating atomistic data, specifically from MD simulations.