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. The formation of the slush-like polar state at the nanoscale in cation-doped BaTiO3 films is simulated by phase field simulation and confirmed by aberration-corrected scanning transmission electron microscopy. The large polarization and the delayed polarization saturation lead to greatly enhanced energy density of 80 J/cm3 and transfer efficiency of 85% over a wide temperature range. Such a data-driven design recipe for a slush-like polar state is generally applicable to quickly optimize functionalities of ferroelectric materials.

Knowledge-Based Descriptor for the Compositional Dependence of the Phase Transition in BaTiO3-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 (Tc) on single doping elements in BaTiO3 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. We compare δτ to other commonly used descriptors such as the tolerance factor, electronegativity, and ionic displacement. By using regression analysis on our assembled experimental data, we show how it outperforms other descriptors. We use the trained machine-learned models to predict compositions in our search space with the largest ferroelectric, dielectric, and piezoelectric properties, namely, d33, electrostrain, and recoverable energy storage density. We experimentally verify our predictions for Tc and classification into ferroelectrics and relaxors by synthesizing and characterizing six solid solutions in BaTiO3 ferroelectrics. Our definition of δτ can shed light on the design of knowledge-based descriptors in other systems such as Pb-based and Bi-based solid solutions.

Atomic Origin of Interface-Dependent Oxygen Migration by Electrochemical Gating at the LaAlO3-SrTiO3 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 LaAlO3-SrTiO3 system through ex situ IL gating experiments and on-site atomic-resolution characterization. The difference between interface structures leads to the controllable electrochemical oxygen migration by filling oxygen vacancies. The findings not only provide an atomic-scale insight into the origin of interface-dependent electrochemical gating but also demonstrate an effective way of engineering interface structure to control the electrochemical gating.

Electronic and plasmonic phenomena at nonstoichiometric grain boundaries in metallic SrNbO3.

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 SrNbO3 photocatalyst show exotic electronic, optical and plasmonic phenomena in comparison to bulk. Aberration-corrected scanning transmission electron microscopy and first-principles calculations reveal that a Nb-rich grain boundary exhibits an increased carrier concentration with quasi-1D metallic conductivity, and newly induced electronic states contributing to the broad energy range of optical absorption. More importantly, dielectric function calculations reveal extended and enhanced plasmonic excitations compared with bulk SrNbO3. Our results show that non-stoichiometric grain boundaries might be utilized to control the electronic and plasmonic properties in oxide photocatalysis.

Accelerated Search for BaTiO3-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 BaTiO3-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. Such an approach is limiting for multidopant solid solutions and motivates the use of two data-driven machine learning and design strategies with a feedback loop to experiments. Strategy I considers learning and property prediction on all the compounds, and strategy II learns to preselect compounds in the crossover region on which prediction is carried out. By performing only two active learning loops via strategy II, the compound (Ba0.86Ca0.14)(Ti0.79Zr0.11Hf0.10)O3 is synthesized with the largest energy storage density ≈73 mJ cm-3 at a field of 20 kV cm-1, and an insight into the relative performance of the strategies using varying levels of knowledge is provided.

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. The present work effectively enriches our understanding of the underlying physical principles governing glassy disordered materials.

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. Here, we use La0.9Sr0.1MnO3 (LSMO), a lightly doped manganite close to the vertical phase boundary, as a model system to demonstrate that both epitaxial strain and cation stoichiometry induced by strain relaxation contribute to functionality tuning. The thinner LSMO films are metallic with a greatly enhanced TC which is 97 K higher than the bulk value. Such anomalies in TC and transport cannot be fully explained by the epitaxial strain alone. Detailed microstructure analysis indicates La deficiency in thinner films and twin domain formation in thicker films. Our results have revealed that both epitaxial strain and strain relaxation induced stoichiometry/microstructure modulations contribute to the modified functional properties in lightly doped manganite perovskite thin films.

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: CaZrO3 modified (K, Na)NbO3 and strontium niobate (SNO), respectively. In CaZrO3 modified (K,Na)NbO3 thin films, a tetragonal tungsten bronze phase was found, with a sharp boundary with the perovskite phase. In SNO thin films, several kinds of point defects and antiphase boundaries are commonly observed. In addition, a strongly Sr deficient phase, SrNb2O6, precipitates inside the SrNbO3 phase with a coherent interface. The different oxidation states of Nb in SrNbO3 and SrNb2O6 were revealed from the O K edge. Our characterization of the point defects and extended defects in niobate thin films offers practical guidelines for thin film deposition or discovery of defect-based novel functionalities.

The Search for BaTiO3-Based Piezoelectrics With Large Piezoelectric Coefficient Using Machine Learning.

We employ a data-driven approach to search for BaTiO3-based piezoelectrics with large piezoelectric coefficient d33. Our approach uses a surrogate model to make predictions of d33 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. This adaptive design strategy is iterated five times and in each iteration, four new compounds are synthesized based on the two different design selection criteria. The best new compound found in this work is (Ba0.85Ca0.15)(Ti0.91Zr0.09)O3 with a d33 of 362 pC/N, compared to the best compound BCT-0.5BZT in the training data with a d33 of ~610 pC/N. Our conclusion from this study is that although our model describes well most of the available d33 data, the especially large value for BCT-0.5BZT is difficult to fit with any surrogate model and emphasizes the need to combine a physics-based approach with a pure data-driven approach used in this study.

Accelerated Discovery of Large Electrostrains in BaTiO3 -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 BaTiO3 (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 (Ba0.84 Ca0.16 )(Ti0.90 Zr0.07 Sn0.03 )O3 with the largest electrostrain of 0.23% in the BTO family. Using Landau theory and insights from density functional theory, it is uncovered that the observed large electrostrain is due to the presence of Sn, which allows for the ease of switching of tetragonal domains under an electric field.

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. We demonstrate this for a high temperature ternary Ti-Pd-Cr SMA by achieving both a small ΔT and high transformation temperature. We propose two possible underlying physics governing the reduction in ΔT. One is that the interfacial strain is accommodated at the austenite/martensite interface via coexistence of B19 and 9R martensites. The other is that one of transformation eigenvalues equal to 1, i.e., λ2 = 1, indicating a perfect coherent interface between austenite and martensite. Our results are not limited to Ti-Pd-Cr SMAs but potentially provide a strategy for searching for SMAs with small thermal hysteresis.

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. Our strategy uses inference and global optimization to balance the trade-off between exploitation and exploration of the search space. We demonstrate this by finding very low thermal hysteresis (ΔT) NiTi-based shape memory alloys, with Ti50.0Ni46.7Cu0.8Fe2.3Pd0.2 possessing the smallest ΔT (1.84 K). We synthesize and characterize 36 predicted compositions (9 feedback loops) from a potential space of ∼800,000 compositions. Of these, 14 had smaller ΔT than any of the 22 in the original data set.