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The singular change of the order parameter at the first order martensitic transformation (MT) temperature restricts the caloric response to a narrow temperature range. Here the MT is tuned into a sluggish strain glass transition by defect doping and a large elastocaloric effect appears in a wide temperature range. Moreover, an inverse elastocaloric effect is observed in the strain glass alloy with history of zero-field cooling and is attributed to the slow dynamics of the nanodomains in response to the external stress. This study offers a design recipe to expand the temperature range for good elastocaloric effect.

Using data science tools including machine learning and statistical analysis, the effects of multiple chemical doping on the energy storage performance of barium titanate based ceramics are investigated from both quantitative and qualitative perspectives.

In this paper, we develop a data-driven machine learning (ML) approach to predict the adiabatic temperature change (ΔT) in BaTiO3-based ceramics as a function of chemical composition, temperature, and applied electric field. The data set was curated from a survey of published electrocaloric measurements. Each chemical composition was represented by elemental descriptors of A-site and B-site elements. Pair-wise statistical correlation analysis was used to remove linearly correlated descriptors. We trained two separate regression-based ML models for indirect and direct measurements and found that both are capable of capturing the general trend of the temperature vs ΔT curve for various applied electric fields. We then complemented the regression models with a classification learning model that predicts the expected phase as a function of chemical composition and temperature. The combined regression and classification learning ML models predict a global maxima in ΔT near rhombohedral to cubic or tetragonal to cubic phase transition regions. An interactive, open source web application is developed to enable interested users to query our trained models and accelerate the design of novel BaTiO3-based ceramics with targeted phase and ΔT properties for electrocaloric applications.

Descriptors play a central role in constructing composition-structure-property relationships to guide materials design. We propose a materials descriptor, δτ , for the composition dependence of the...

Materials informatics employs machine learning (ML) models to map the relationship between a targeted property and various materials descriptors, providing new avenues to accelerate the discovery of new materials. However, the possible ML models and materials descriptors are numerous, and a rational recipe to rapidly choose the best combination of the two is needed. In the present study, we propose a systematic framework that utilizes a genetic algorithm (GA) to efficiently select the ML model and materials descriptors from a huge number of alternatives and demonstrated its efficiency on two phase formation problems in high entropy alloys (HEAs). The optimized classification model allows an accuracy for identifying solid-solution and non-solid-solution HEAs to be up to 88.7% and further for recognizing body-centered-cubic (BCC), face-centered-cubic (FCC), and dual-phase HEAs to reach 91.3%. Furthermore, by employing an active learning approach, several HEAs with largest classification uncertainties were selected, experimentally synthesized and phase-identified, and augmented to the initial dataset to iteratively improve the ML model. The method serves as a general algorithm to select materials descriptors and ML models for various material problems including classification and optimization of targeted properties.

A ferroelectric phase transition under an external bias electric field after field cooling and zero field cooling in barium titanate single crystals shows scale invariant nucleation and growth of complex domain structures. The avalanche energy exponents vary with the external bias, depending on the cooling history of the sample under applied field or zero field cooling. After field cooling, resulting in a single domain sample, the energy exponent is near the integrated mean field value of 5/3, namely, 1.68 ± 0.022. The sample after field cooling shows the same exponent with an external bias electric field range of up to 4.5 kV/cm. The exponent of a multi-domain sample, after zero field cooling, decreases from ε = 1.85 to the fixed point of 1.66 under high fields. The different behavior is attributed to the greater complexity of domain patterns in the multi-domain sample, which reduces to a single domain state under strong applied fields. Compared with the single-domain state, the multi-domain state has more domain boundaries and the intersections formed by these domain boundaries. These domain boundaries and their intersections will hinder the movement of the phase boundary and act as a pinning effect on the front of the phase interface. The effect is to generate more small energy signals, making the critical exponent high. At the same time, the aftershock time distribution (Omori law) remains the same for all switching conditions with an Omori exponent near −1 and switching time correlations of −1 ± 0.05 for short times (1 s).

In this paper, we develop a data-driven machine learning (ML) approach to predict the adiabatic temperature change (Δ

Strain glass is a glassy state with frozen ferroelastic/martensitic nanodomains in shape memory alloys, yet its nature remains unclear. Here, we report a glassy feature in strain glass that was thought to be only present in structural glasses. An abnormal hump is observed in strain glass around 10 K upon normalizing the specific heat by cubed temperature, similar to the boson peak in metallic glass. The simulation studies show that this boson-peak-like anomaly is caused by the phonon softening of the non-transforming matrix surrounding martensitic domains, which occurs in a transverse acoustic branch not associated with the martensitic transformation displacements. Therefore, this anomaly neither is a relic of van Hove singularity nor can be explained by other theories relying on structural disorder, while it verifies a recent theoretical model without any assumptions of disorder. This work might provide fresh insights in understanding the nature of glassy states and associated vibrational properties., Strain glass is a new glassy state characterized by frozen ferroelastic nanodomains. Here, the authors discover a low-temperature feature in the specific heat of a strain glass, which is similar to the well-known boson peak anomaly of structural glasses, but cannot be explained by existing mechanisms.

Traditional strategies for designing new materials with targeted property including methods such as trial and error, and experiences of domain experts, are time and cost consuming. In the present study, we propose a machine learning design system involving three features of machine learning modeling, compositional design and property prediction, which can accelerate the discovery of new materials. We demonstrate better efficiency of on a rapid compositional design of high-performance copper alloys with a targeted ultimate tensile strength of 600–950 MPa and an electrical conductivity of 50.0% international annealed copper standard. There exists a good consistency between the predicted and measured values for three alloys from literatures and two newly made alloys with designed compositions. Our results provide a new recipe to realize the property-oriented compositional design for high-performance complex alloys via 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.

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.

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. The approach is proven by predicting and synthesizing the ferroelectric ceramic system (1‐ω)(Ba0.61Ca0.28Sr0.11TiO3)‐ω(BaTi0.888Zr0.0616Sn0.0028Hf0.0476O3) with a relatively high transition temperature and triple point, as well as the NiTi‐based pseudo‐binary phase diagram (1‐ω)(Ti0.309Ni0.485Hf0.20Zr0.006)‐ω(Ti0.309Ni0.485Hf0.07Zr0.068Nb0.068) designed for high transition temperature (ω ⩽ 1). Each phase diagram is validated and optimized through only three new experiments. The complexity of these compounds is beyond the reach of today's computational methods., A machine learning‐based approach is proposed to predict all possible phase diagrams of a given multi‐component system from a high‐dimensional virtual space. By quickly screening the space, a specific phase diagram with given design criteria can be constructed. Bayesian experimental design is then employed to refine the phase diagram with as few experiments as possible.

Large-scale applications of elastocaloric cooling demand bulk materials showing both large adiabatic temperature change (∆Tad) and low-fatigue characteristics at room temperature. Here we synthesize a bulk Ti49.2Ni40.8Cu10 polycrystalline alloy microstructurally featured by nanocrystalinity and epitaxially related Ti(Ni,Cu) 2 nanoprecipitates through cold-rolling and aging treatment. It exhibits a large ∆Tad of 13.8 K and a high coefficient of performance value of 13 at room temperature. Moreover, the degradation of ∆Tad is only 0.3 K after 450 tensile cycles. The results indicate that the alloy offers a good balance of multiple objectives, holding promise for solid-state refrigeration applications. We attribute the favorable properties to the enhanced reversibility of martensitic transformation during stress cycling, aided by the internal epitaxy-generated stress at the interface between the Ti(Ni,Cu)2 nanoprecipitates and matrix, together with grain refinement.

In the present study, the reversible adiabatic temperature change ( Δ T a d ) of shape memory alloys was shown to be proportional to the mechanical work released by the reverse martensitic transformation during unloading ( Δ W u ). As there exists a considerable amount of data ( Δ W u ) on superelastic stress-strain measurements in shape memory alloys, the proposed relationship allows us to predict Δ T a d without caloric measurements. The estimated Δ T a d from the Δ W u of different Ti-Ni alloys shows good linear relationship with the directly measured values. Moreover, following such a design criterion and by tuning of composition and thermo-mechanical treatment, a group of Ti-Ni binary shape memory alloys with directly measured Δ T a d larger than 35 K under tension were achieved. The large Δ T a d and Δ W u can be ascribed to the grain refinement and the heterogeneous internal stress fields after the thermo-mechanical treatment, which have enhanced the critical stress of superelasticity and recoverability of martensitic transformation during unloading.

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 (

Typical ferroelectrics possess a large spontaneous polarization Ps but simultaneously a large remnant polarization Pr as well, resulting in an inferior energy storage density.A mechanism that can reduce the Pr while maintain the Ps is demanded to enhance the energy storage property of ferroelectrics.In the present study, it is shown that after acceptor doping and aging treatment, the domain switching in ferroelectrics becomes reversible, giving rise to a pinched double hysteresis loop. The pinched loop with a large Ps and a small Pr thus results in an enhanced energy storage density. The physics behind is a defect induced internal field that provides a restoring force for the domains to switch back.The idea is demonstrated through a time-dependent Ginzburg-Landau simulation as well as experimental measurements in BaTiO$_3$ based single crystal and ceramics. The mechanism is general and can be applied to various ferroelectrics, especially the environment-friendly ones., under submission to RCS JMCA

Hamiltonian parameters estimation is crucial in condensed matter physics, but is time‐ and cost‐consuming. High‐resolution images provide detailed information of underlying physics, but extracting Hamiltonian parameters from them is difficult due to the huge Hilbert space. Here, a protocol for Hamiltonian parameters estimation from images based on a machine learning (ML) architecture is provided. It consists in learning a mapping between spin configurations and Hamiltonian parameters from a small amount of simulated images, applying the trained ML model to a single unexplored experimental image to estimate its key parameters, and predicting the corresponding materials properties by a physical model. The efficiency of the approach is demonstrated by reproducing the same spin configuration as the experimental one and predicting the coercive field, the saturation field, and even the volume of the experiment specimen accurately. The proposed approach paves a way to achieve a stable and efficient parameters estimation., Magnetic parameters can be efficiently estimated from an experimental observation of spin configuration by combining machine learning and micro‐magnetic simulation. The method includes learning a mapping from spin configurations to magnetic parameters on a small amount of micro‐magnetic simulated images and applying the trained machine learning model to a single unexplored experimental image to estimate its key parameters.

Designing a material with multiple desired properties is a great challenge, especially in a complex material system. Here, we propose a material design strategy to simultaneously optimize multiple targeted properties of multi-component Co-base superalloys via machine learning. The microstructural stability, γ′ solvus temperature, γ′ volume fraction, density, processing window, freezing range, and oxidation resistance were simultaneously optimized. A series of novel Co-base superalloys were successfully selected and experimentally synthesized from >210,000 candidates. The best performer, Co-36Ni-12Al-2Ti-4Ta-1W-2Cr, possesses the highest γ′ solvus temperature of 1266.5 °C without the precipitation of any deleterious phases, a γ′ volume fraction of 74.5% after aging for 1000 h at 1000 °C, a density of 8.68 g cm−3 and good high-temperature oxidation resistance at 1000 °C due to the formation of a protective alumina layer. Our approach paves a new way to rapidly design multi-component materials with desired multi-performance functionality.

The method of Bayesian Global Optimization, using a surrogate model and a utility function, is reviewed and its application toward finding alloys with targeted properties, such as high transition temperature and very small thermal hysteresis, is discussed. We also address the calculation of estimates of uncertainties from data and compare the estimates obtained by using standard deviation and the Infinitesimal Jackknife on two experimental datasets, one for piezoelectrics and one for shape-memory alloys (SMAs). We also discuss the importance of the utility function for selection and ranking next candidate data points in minimizing the number of evaluations to nd optima. Finally, we illustrate how these ideas can be applied to discovering new SMAs with high transition temperatures and very small thermal hysteresis by reviewing the results of the synthesis and characterization of alloys carried out previously.

In addition to being determined by its chemical composition and processing conditions, the performance of a material is also affected by the variables of its service space, including temperature, pressure, and frequency. A rapid means to estimate the performance of a material in its service space is urgently required to accelerate the screening of materials with targeted performance. In the present study, a materials informatics approach is proposed to rapidly predict performance within a service space based on existing data. We utilize an active learning loop, which employs an ensemble machine learning method to predict the performance, followed by a Bayesian experimental design to minimize the number of experiments for refinement and validation. This approach is demonstrated by predicting the damping properties of a ZE62 magnesium alloy in a service space defined by frequency, strain amplitude, and temperature based on the available data for other magnesium alloys. Several utility functions that recommend a particular experiment to refine the estimates of the service space are used and compared. In particular, the standard deviation is found to reduce the prediction error most efficiently. After augmenting the database with nine new experimental measurements, the uncertainties associated with the predicted damping capacities are largely reduced. Our method allows us to forecast the properties in the service space of a given material by rapid refinement of the predictions via experiment measurements.

A relatively large bipolar strain of 0.23 % was recently reported in 3 % Sn4+ doped (BaTi0.8Zr0.2)O3-x (Ba0.7Ca0.3)TiO3 (BZT-xBCT) ceramics under a field of 20 kV/cm. This strain is 53 % higher than that in BZT-50BCT (0.15 % at 20 kV/cm). To systematically study the mechanism and origin of the large electrostrain enhancement in Sn4+ doped BZT-53.3BCT, we develop a parameterized Landau free energy model and perform phase field simulations. Our results indicate that the softening of the elastic modulus C ′ = C 11 − C 12 accompanied by the reduction of anisotropy energy due to the 3 % Sn4+ doping is primarily responsible for the large electrostrain enhancement.