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.

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.

Atomic Origin of Interface-Dependent Oxygen Migration by Electrochemical Gating at the LaAlO-SrTiO Heterointerface.

Electrical control of material properties based on ionic liquids (IL) has seen great development and emerging applications in the field of functional oxides, mainly understood by the electrostatic and electrochemical gating mechanisms. Compared to the fast, flexible, and reproducible electrostatic gating, electrochemical gating is less controllable owing to the complex behaviors of ion migration. Here, the interface-dependent oxygen migration by electrochemical gating is resolved at the atomic scale in the LaAlO-SrTiO system through ex situ IL gating experiments and on-site atomic-resolution characterization.

Electronic and plasmonic phenomena at nonstoichiometric grain boundaries in metallic SrNbO.

Grain boundaries could exhibit exceptional electronic structure and exotic properties, which are determined by a local atomic configuration and stoichiometry that differs from the bulk. However, optical and plasmonic properties at the grain boundaries in metallic oxides have rarely been discussed before. Here, we show that non-stoichiometric grain boundaries in the newly discovered metallic SrNbO photocatalyst show exotic electronic, optical and plasmonic phenomena in comparison to bulk.

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.

Percolated Strain Networks and Universal Scaling Properties of Strain Glasses.

Strain glass is being established as a conceptually new state of matter in highly doped alloys, yet the understanding of its microscopic formation mechanism remains elusive. Here, we use a combined numerical and experimental approach to establish, for the first time, that the formation of strain glasses actually proceeds via the gradual percolation of strain clusters, namely, localized strain clusters that expand to reach the percolating state. Furthermore, our simulation studies of a wide variety of specific materials systems unambiguously reveal the existence of distinct scaling properties and universal behavior in the physical observables characterizing the glass transition, as obeyed by many existing experimental findings.

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.

Atomic scale characterization of point and extended defects in niobate thin films.

Niobium-based oxides have a wide range of applications owing to their rich crystal and electronic structures. Defects at the atomic scale are always unavoidable and will affect their functionalities, especially when in the form of thin films. Here, atomic resolution scanning transmission electron microscopy and electron energy loss spectroscopy have been performed on various defects (point, line, planar defects and segregated phases) in alkaline and alkaline-earth niobate thin films: CaZrO modified (K, Na)NbO and strontium niobate (SNO), respectively.

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.

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.

Accelerated search for materials with targeted properties by adaptive design.

Finding new materials with targeted properties has traditionally been guided by intuition, and trial and error. With increasing chemical complexity, the combinatorial possibilities are too large for an Edisonian approach to be practical. Here we show how an adaptive design strategy, tightly coupled with experiments, can accelerate the discovery process by sequentially identifying the next experiments or calculations, to effectively navigate the complex search space.