Enhanced Piezoelectric Response at Nanoscale Vortex Structures in Ferroelectrics.

The piezoelectric response is a measure of the sensitivity of a material's polarization to stress or its strain to an applied field. Using X-ray Bragg coherent diffraction imaging, we observe that topological vortices are the source of a 5-fold enhancement of the piezoelectric response near the vortex core. The vortices form where several low-symmetry ferroelectric phases and phase boundaries coalesce.

Periodic Intermittent Adaptive Control with Saturation for Pinning Quasi-Consensus of Heterogeneous Multi-Agent Systems with External Disturbances.

A periodic intermittent adaptive control method with saturation is proposed to pin the quasi-consensus of nonlinear heterogeneous multi-agent systems with external disturbances in this paper. A new periodic intermittent adaptive control protocol with saturation is designed to control the internal coupling between the follower agents and the feedback gain between the leader and the follower. In particular, we use the saturation adaptive law: when the quasi-consensus error converges to a certain range, the adaptive coupling edge weight and the adaptive feedback gain will not be updated.

Machine Learning-Enabled Superior Energy Storage in Ferroelectric Films with a Slush-Like Polar State.

Heterogeneities in structure and polarization have been employed to enhance the energy storage properties of ferroelectric films. The presence of nonpolar phases, however, weakens the net polarization. Here, we achieve a slush-like polar state with fine domains of different ferroelectric polar phases by narrowing the large combinatorial space of likely candidates using machine learning methods.

Determining Multi-Component Phase Diagrams with Desired Characteristics Using Active Learning.

Herein, we demonstrate how to predict and experimentally validate phase diagrams for multi-component systems from a high-dimensional virtual space of all possible phase diagrams involving several elements based on small existing experimental data. The experimental data for bulk phases for known systems represents a sampling from this space, and screening the space allows multi-component phase diagrams with given design criteria to be built. This approach uses machine learning methods to predict phase diagrams and Bayesian experimental design to minimize experiments for refinement and validation, all within an active learning loop.

Knowledge-Based Descriptor for the Compositional Dependence of the Phase Transition in BaTiO-Based Ferroelectrics.

Descriptors play a central role in constructing composition-structure-property relationships to guide materials design. We propose a material descriptor, δτ, for the composition dependence of the Curie temperature () on single doping elements in BaTiO ferroelectrics, which is then generalized to a linear combination of multiple dopants in the solid solutions. The descriptor δτ depends linearly on the Curie temperature and also serves to separate the ferroelectric phase from the relaxor phase.

Accelerated Search for BaTiO-Based Ceramics with Large Energy Storage at Low Fields Using Machine Learning and Experimental Design.

The problem that is considered is that of maximizing the energy storage density of Pb-free BaTiO-based dielectrics at low electric fields. It is demonstrated that how varying the size of the combinatorial search space influences the efficiency of material discovery by comparing the performance of two machine learning based approaches where different levels of physical insights are involved. It is started with physics intuition to provide guiding principles to find better performers lying in the crossover region in the composition-temperature phase diagram between the ferroelectric phase and relaxor ferroelectric phase.

Enhanced magnetism in lightly doped manganite heterostructures: strain or stoichiometry?

Lattice mismatch induced epitaxial strain has been widely used to tune functional properties in complex oxide heterostructures. Apart from the epitaxial strain, a large lattice mismatch also produces other effects including modulations in microstructure and stoichiometry. However, it is challenging to distinguish the impact of these effects from the strain contribution to thin film properties.

The Search for BaTiO-Based Piezoelectrics With Large Piezoelectric Coefficient Using Machine Learning.

We employ a data-driven approach to search for BaTiO-based piezoelectrics with large piezoelectric coefficient d. Our approach uses a surrogate model to make predictions of d with uncertainties, followed by a design step that selects the next optimal compound to synthesize. We compare several combinations of choices of the model and design selection strategies on the training data assembled from many experiments that we have previously performed, and we choose the best two performers for guiding new experiments.

Accelerated Discovery of Large Electrostrains in BaTiO -Based Piezoelectrics Using Active Learning.

A key challenge in guiding experiments toward materials with desired properties is to effectively navigate the vast search space comprising the chemistry and structure of allowed compounds. Here, it is shown how the use of machine learning coupled to optimization methods can accelerate the discovery of new Pb-free BaTiO (BTO-) based piezoelectrics with large electrostrains. By experimentally comparing several design strategies, it is shown that the approach balancing the trade-off between exploration (using uncertainties) and exploitation (using only model predictions) gives the optimal criterion leading to the synthesis of the piezoelectric (Ba Ca )(Ti Zr Sn )O with the largest electrostrain of 0.

Accelerated search for BaTiO3-based piezoelectrics with vertical morphotropic phase boundary using Bayesian learning.

An outstanding challenge in the nascent field of materials informatics is to incorporate materials knowledge in a robust Bayesian approach to guide the discovery of new materials. Utilizing inputs from known phase diagrams, features or material descriptors that are known to affect the ferroelectric response, and Landau-Devonshire theory, we demonstrate our approach for BaTiO-based piezoelectrics with the desired target of a vertical morphotropic phase boundary. We predict, synthesize, and characterize a solid solution, (BaCa)TiO-Ba(TiZr)O, with piezoelectric properties that show better temperature reliability than other BaTiO-based piezoelectrics in our initial training data.

Design of High Temperature Ti-Pd-Cr Shape Memory Alloys with Small Thermal Hysteresis.

The large thermal hysteresis (ΔT) during the temperature induced martensitic transformation is a major obstacle to the functional stability of shape memory alloys (SMAs), especially for high temperature applications. We propose a design strategy for finding SMAs with small thermal hysteresis. That is, a small ΔT can be achieved in the compositional crossover region between two different martensitic transformations with opposite positive and negative changes in electrical resistance at the transformation temperature.