Ferroelectrics have a spontaneous electrical polarization that is arranged into domains and can be reversed by an externally applied field. This high versatility makes them useful in enabling components such as capacitors, sensors, and actuators. The key to tuning their dielectric, piezoelectric, and electromechanical performance is to control the domain structure and the dynamics of the domain walls. In fixed compositions, this is often realized by chemical doping. In addition, structural and microstructural parameters, such as grain size, degree of crystallographic texture or porosity play a key role. A major breakthrough in the field came with the fundamental understanding of the link between the local electric and mechanical driving forces and domain wall motion. Here, the impact of structure and microstructure on these driving forces is reviewed and an engineering toolbox is introduced. An overview of advances in the understanding of domain wall motion on the micro- and nanoscale is provided and discussed in terms of the macroscopic functional performance of polycrystalline ferroelectrics/ferroelastics. In addition, a link to theoretical and computational models is established. The review concludes with a discussion about beyond state-of-the-art characterization techniques, new approaches, and future directions toward non-conventionally ordered ferroelectrics for next-generation nanoelectronics and energy-storage applications.
Fatigue failure in ferroelectrics has been intensively investigated in the past few decades. Most of the mechanisms discussed for ferroelectric fatigue have been built on the "hypothesis of variation in charged defects", which however are rarely evidenced by experimental observation. Here, using a combination of complex impedance spectra techniques, piezoresponse force microscopy and first-principles theory, we examine the microscopic evolution and redistribution of charged defects during the electrical cycling in BiFeO3 thin films. The dynamic formation and melting behaviors of oxygen vacancy…
Viscoplastic flow of polycrystalline metallic materials is the result of motion and interaction of dislocations, line defects of the crystalline structure. In the microstructural (physics-based) constitutive model presented in this paper, the main underlying microstructural processes influencing viscoplastic deformation and mechanical properties of metals in cold and warm regimes are statistically described by the introduced sets of postulates/axioms for continuum dislocation dynamics (CDD). Three microstructural (internal) state variables (MSVs) are used for statistical quantifications of dif…
In this research, atomistic molecular dynamics simulations are combined with mesoscopic phase-field computational methods in order to investigate phase-transformation in polycrystalline Aluminum microstructure. In fact, microstructural computational modeling of engineering materials could help to optimize their mechanical properties for industrial applications (e.g. directional solidification for turbine blades). As a result, a multiscale modeling approach is developed to find a relation between manufacturing variables (e.g. temperature) and microstructural properties of crystalline materials …
While the microscopic structure of defected solid crystalline materials has significant impact on their physical properties, efficient and accurate determination of a given polycrystalline microstructure remains a challenge. In this paper we present a highly generalizable and reliable variational method to achieve this goal for two-dimensional crystalline and quasicrystalline materials. The method is benchmarked and optimized successfully using a variety of large-scale systems of defected solids, including periodic structures and quasicrystalline symmetries to quantify their microstructural ch…
Many time-dependent deformation processes at elevated temperatures produce significant concurrent microstructure changes that can alter the mechanical properties in a profound manner. Such microstructure evolution is usually absent in mesoscale deformation models and simulations. Here we present an integrated full-field modeling scheme that couples the mechanical response with the underlying microstructure evolution. As a first demonstration, we integrate a fast Fourier transform-based elasto-viscoplastic (FFT-EVP) model with a phase-field (PF) recrystallization model, and carry out three-dime…
In the present work, we investigate the magnetic properties of ferrimagnetic and noninteracting maghemite (g-Fe2O3) hollow nanoparticles obtained by the Kirkendall effect. From the experimental characterization of their magnetic behavior, we find that polycrystalline hollow maghemite nanoparticles are characterized by low superparamagnetic-to-ferromagnetic transition temperatures, small magnetic moments, significant coercivities and irreversibility fields, and no magnetic saturation on external magnetic fields up to 5 T. These results are interpreted in terms of the microstructural parameters …
As data-driven methods rise in popularity in materials science applications, a key question is how these machine learning models can be used to understand microstructure. Given the importance of process-structure-property relations throughout materials science, it seems logical that models that can leverage microstructural data would be more capable of predicting property information. While there have been some recent attempts to use convolutional neural networks to understand microstructural images, these early studies have focused only on which featurizations yield the highest machine learni…
PolyMicros: Bootstrapping a Foundation Model for Polycrystalline Material Structure
School of Computational Science and Engineering（计算科学与工程学院）；Georgia Institute of Technology（佐治亚理工学院）；Center for Integrated Nanotechnology（集成纳米技术中心）；Sandia National Laboratories（沙伊达国家实验室）；George W. Woodruff School of Mechanical Engineering（乔治·W·伍德鲁夫机械工程学院）
Heusler compounds form a numerous class of intermetallics, which include two families of compositions ABC and AB2C, usually referred to as half- and full-Heusler compounds, respectively. Given their tunable electronic properties, made possible by adjusting the chemical composition, these materials are currently considered for the possible use in sustainable technologies such as solar energy and thermoelectric conversion. According to theoretical predictions, Sb substitution in the TiFe2Sn full-Heusler compound is thought to yield band structure modifications that should enhance the thermoelect…
Consistent machine learning for topology optimization with microstructure-dependent neural network material models
Delft University of Technology（代尔夫特理工大学）；Princeton University（普林斯顿大学）；Princeton Materials Institute (PMI), Princeton University（普林斯顿材料研究所（PMI），普林斯顿大学）；Brown University（布朗大学）
In order to address the growth of crystals in veins, a multiphase-field model is used to capture the dynamics of crystals precipitating from a super-saturated solution. To gain a detailed understanding of the polycrystal growth phenomena in veins, we investigate the influence of various boundary conditions on crystal growth. In particular, we analyze the formation of vein microstructures resulting from the free growth of crystals as well as crack-sealing processes. We define the crystal symmetry by considering the anisotropy in surface energy to simulate crystals with flat facets and sharp cor…
Stochastic microstructure reconstruction has become an indispensable part of computational materials science, but ongoing developments are specific to particular material systems. In this paper, we address this generality problem by presenting a transfer learning-based approach for microstructure reconstruction and structure-property predictions that is applicable to a wide range of material systems. The proposed approach incorporates an encoder-decoder process and feature-matching optimization using a deep convolutional network. For microstructure reconstruction, model pruning is implemented …
