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Recent advances in microelectromechanical systems often require multifunctional materials, which are designed so as to optimize more than one property. Using density functional theory calculations for alloyed nitride systems, we illustrate how co-alloying a piezoelectric material (AlN) with different nitrides helps tune both its piezoelectric and mechanical properties simultaneously. Wurtzite AlN-YN alloys display increased piezoelectric response with YN concentration, accompanied by mechanical softening along the crystallographic c direction. Both effects increase the electrome-chanical coupling coefficients relevant for transducers and actuators. Resonator applications, however, require superior stiffness, thus leading to the need to decouple the increased piezoelectric response from the softened lattice. We show that co-alloying of AlN with YN and BN results in improved elastic properties while retaining some of the piezoelectric enhancements from YN alloying. This finding may lead to new avenues for tuning the design properties of piezoelectrics through composition-property maps. [ABSTRACT FROM AUTHOR]

Small metal clusters are of fundamental scientific interest and of tremendous significance in catalysis. These nanoscale clusters display diverse geometries and structural motifs depending on the cluster size; a knowledge of this size‐dependent structural motifs and their dynamical evolution has been of longstanding interest. Given the high computational cost of first‐principles calculations, molecular modeling and atomistic simulations such as molecular dynamics (MD) has proven to be an important complementary tool to aid this understanding. Classical MD typically employ predefined functional forms which limits their ability to capture such complex size‐dependent structural and dynamical transformation. Neural Network (NN) based potentials represent flexible alternatives and in principle, well‐trained NN potentials can provide high level of flexibility, transferability and accuracy on‐par with the reference model used for training. A major challenge, however, is that NN models are interpolative and requires large quantities (∼104 or greater) of training data to ensure that the model adequately samples the energy landscape both near and far‐from‐equilibrium. A highly desirable goal is minimize the number of training data, especially if the underlying reference model is first‐principles based and hence expensive. Here, we introduce an active learning (AL) scheme that trains a NN model on‐the‐fly with minimal amount of first‐principles based training data. Our AL workflow is initiated with a sparse training dataset (∼ 1 to 5 data points) and is updated on‐the‐fly via a Nested Ensemble Monte Carlo scheme that iteratively queries the energy landscape in regions of failure and updates the training pool to improve the network performance. Using a representative system of gold clusters, we demonstrate that our AL workflow can train a NN with ∼ 500 total reference calculations. Using an extensive DFT test set of ∼1100 configurations, we show that our AL‐NN is able to accurately predict both the DFT energies and the forces for clusters of a myriad of different sizes. Our NN predictions are within 30 meV/atom and 40 meV/Å of the reference DFT calculations. Moreover, our AL‐NN model also adequately captures the various size‐dependent structural and dynamical properties of gold clusters in excellent agreement with DFT calculations and available experiments. We finally show that our AL‐NN model also captures bulk properties reasonably well, even though they were not included in the training data. [ABSTRACT FROM AUTHOR]

Zener pinning refers to the dispersion of fine particles which influences grain size distribution via movement of grain boundaries in a polycrystalline material. Grain size distribution in polycrystals has a significant impact on their properties including physical, chemical, mechanical, and optical to name a few. We explore the use of Phase-field modeling and machine-learning techniques to understand and improve the control of grain size distribution via Zener pinning in polycrystalline materials. We develop a machine learning model that determines the relative importance of various parameters to exercise microstructure control via Zener pinning. Our workflow combines high-throughput phase-field simulations and machine learning to address the computational bottlenecks associated with large-scale simulations as well as identify features necessary for microstructure control in polycrystals. A random forest (RF) regression model was developed to predict grain sizes based on five Phase-field model parameters, achieving an average prediction error of 0.72 nm for the training data and 1.44 nm for the test data. The importance of the input parameters is analyzed using the SHapley Additive exPlanations (SHAP) approach which reveals that diffusivity, volume fraction, and particle diameter are the most important parameters in determining the final grain size. These findings will allow us to select the best second-phase particles, optimize grain size distributions and thus design microstructures with the desired properties. The developed method is a highly versatile and generalizable approach that can be used to assess the combined effects of individual features in the presence of multiple variables. [Display omitted] [ABSTRACT FROM AUTHOR]

Graphical abstract Abstract A novel phase field model has been developed to study the effect of coherent precipitate on the Zener pinning of matrix grain boundaries. The model accounts for misfit strain between precipitate and matrix as well as the elastic inhomogeneity and anisotropy between them. The results show that increase in elastic misfit, elastic inhomogeneity, and elastic anisotropy increases the coarsening rate of the precipitates. Increased coarsening of precipitates in turn decreases the pinning of grain boundaries. Therefore, increase in misfit strain, elastic inhomogeneity and anisotropy mostly negatively affect the Zener pinning through coherent precipitate. This study shows elastic anisotropy gives rise to the needle shape precipitate. It has also been shown that these needle shaped precipitates are not very effective in Zener pinning. This study provides an understanding into the effect of coherent precipitate on the Zener pinning of matrix grain boundaries. To design a material with smallest possible grain size, coherent precipitate with least lattice misfit and highest elastic modulus will be most effective. [ABSTRACT FROM AUTHOR]

While it is well-established that ionic conduction in lithium aluminosilicates proceeds via hopping of Li ions, the nature of the various hoping-based mechanisms in different temperature regimes has not been fully elucidated. The difficulties associated with investigating the conduction have to do with the presence of grains and grain boundaries of different orientations in these usually polycrystalline materials. Herein, we use electrochemical impedance spectroscopy ( EIS) to investigate the ion conduction mechanisms in β-eucryptite, which is a prototypical lithium aluminosilicate. In the absence of significant structural transitions in grain boundaries, we find that there are three conduction regimes for the one-dimensional ionic motion along the c axis channels in the grains, and determine the activation energies for each of these temperature regimes. Activation energies computed from molecular statics calculations of the potential energy landscape encountered by Li ions suggest that at temperatures below 440°C conduction proceeds via cooperative or correlated motion, in agreement with established literature. Between 440°C and 500°C, the activation barriers extracted from EIS measurements are large and consistent with those from atomistic calculations for uncorrelated Li ion hopping. Above 500°C the activation barriers decrease significantly, which indicates that after the transition to the Li-disordered phase of β-eucryptite, the Li ion motion largely regains the correlated character. [ABSTRACT FROM AUTHOR]

Elastic constants characterize the stiffness of a material. A linear relationship between the stress and strain tensors in the limit of infinitesimal deformation is used to define and compute the elastic constants. Typically, this is computed at zero temperature by deforming the simulation box in one of the six directions and computing the stress tensor. Calculating elastic constants at finite temperature is more challenging owing to large fluctuations and thermal noise. Here, we introduce a semi-automated workflow — Elastemp, to obtain the quasi-harmonic elastic constants of materials as a function of temperature. The workflow integrates with electronic structure packages such as VASP and PHONOPY to get the quasi-harmonic energies. The workflow is capable of estimating the zero temperature elastic constants, thermal expansion coefficients of materials and temperature dependent elastic constants. The predictions for a set of representative test cases with different crystal symmetries (Al, Diamond, Titanium nitride (TiN) and α -Ti) are compared with experiments and used to demonstrate the efficacy of our workflow. [Display omitted] • Workflow to obtain QHA elastic constants of materials with arbitrary symmetry. • Obtains zero temperature elastic constants and checks for mechanical stability. • Calculates thermal expansion coefficient and high temperature elastic constants. • Results are shown to be in good agreement with experimental observations. [ABSTRACT FROM AUTHOR]

Certain alumino-silicates display exotic properties enabled by their framework structure made of corner-sharing tetrahedral rigid units. Using in situ diamond-anvil cell x-ray diffraction (XRD), we study the pressure-induced transformation of β eucryptite, a prototypical alumino-silicate that undergoes a phase transformation at moderate pressures. The atomic structure and symmetry group of the new pressure-stabilized phase has not yet been reported. Based on density functional theory studies and Rietveld analysis of XRD patterns, we find that the new phase belongs to the Pna2 1 space group and report its atomic structure. Furthermore, we discover two other possible pressure-stabilized polymorphs, P1c1 and Pca2 1 . [ABSTRACT FROM AUTHOR]

[Display omitted] Identifying key descriptors and understanding important features across different classes of materials are crucial for machine learning (ML) tools to both predict material properties and reveal the physics underlying any process of interest. Traditionally, the predictive modeling of elastic properties of materials is limited to only a few classes of materials and a small set of ML tools despite the broad applications of these materials. Users now have a broad choice of ML models ranging from simple regression models to graph neural networks (GNN) for predicting structure–property relationships. While in recent years, GNNs have outshined traditional ML models in terms of predictability, their intensive data requirement and lack of interpretability may limit practical applicability. Here, we develop a domain-segmented feature space using a diverse set of material attributes and perform a predictive analysis using state-of-the-art ML tools using elastic modulus prediction as a representative example. By deducing a model-independent overall ranking based on feature importance learned by each model, the knowledge is then transferred to GNNs. Our findings indicate a threshold limit on the predictability of traditional ML models, but our approach of transferring task-specific feature importance knowledge to the GNNs can enhance their performance by reducing their data requirement while retaining considerable accuracy. [ABSTRACT FROM AUTHOR]

[Display omitted] Understanding and predicting accurate property-structure-processing relationships for additively manufactured components is important for both forward and inverse design of robust, reliable parts and assemblies. While direct mapping of process parameters to properties is sometimes plausible, it is often rendered difficult due to poor microstructural control. Exploring the direct relationship between processing conditions and microstructural features can thus provide significant physical insights and aid the overall design process. Here, we develop an automated high-throughput framework to simulate an uncertainty-aware additive manufacturing (AM) process, characterize microstructural images, and extract meaningful features/descriptors. A kinetic Monte Carlo (KMC) based model of the AM process is used to simulate microstructural evolution for a diverse set of experimentally relevant processing conditions. We perform a parametric study to explore the relationship between microstructural features and processing conditions. Our results indicate that a many-to-one mapping can exist between processing conditions and typical descriptors; therefore, multiple descriptors are thus necessary to unambiguously represent microstructural images. Our work provides crucial quantitative and qualitative information that would aid in the selection of features for microstructural images. Featurized microstructures could then be utilized to build data-driven models for predictive control of microstructures and thereby properties of additively manufactured components. [ABSTRACT FROM AUTHOR]

Knowledge of accurate values of elastic modulus of (Al1–xScx)N is required for design of piezoelectric resonators and related devices. Thin films of (Al1–xScx)N across the entire composition space are deposited and characterized. Accuracy of modulus measurements is improved and quantified by removing the influence of substrate effects and by direct comparison of experimental results with density functional theory calculations. The 5%–30% Sc compositional range is of particular interest for piezoelectric applications and is covered at higher compositional resolution here than in previous work. The reduced elastic modulus is found to decrease by as much as 40% with increasing Sc concentration in the wurtzite phase according to both experimental and computational techniques, whereas Sc-rich rocksalt-structured films exhibit little variation in modulus with composition. [ABSTRACT FROM AUTHOR]

Thermal stability of nanocrystalline multilayer thin film is of paramount importance as the applications often involve high temperature. Here we report on the layer instability phenomenon in binary polycrystalline thin film initiating from the grain boundary migrations at higher temperatures using phase-field simulations. Effect of layer thickness, bilayer spacing and the absence of grain boundary are also investigated along with the grain boundary mobility of individual phases on the layer stability. Layer instability in the polycrystalline film is shown to arise from the grain boundary grooving which originates spontaneously from the presence of grain boundaries. Our results show that the growth of the perturbation generated from the differential curvature follows Plateau–Rayleigh instability criterion. Increase in layer thickness, lower bilayer thickness as well as lower grain boundary mobility improve layer stability. Phase-field simulations show similar microstructural evolution as has been observed in our zirconium (Zr)/zirconium nitride (ZrN) system experimentally. Detail analysis performed in this work to understand the mechanisms of layer instability leads us to predict measures which will improve the thermal stability of multilayer nanocrystalline thin film. [ABSTRACT FROM AUTHOR]

Reconfigurable devices offer the ability to program electronic circuits on demand. In this work, we demonstrated on-demand creation of artificial neurons, synapses, and memory capacitors in post-fabricated perovskite NdNiO3 devices that can be simply reconfigured for a specific purpose by single-shot electric pulses. The sensitivity of electronic properties of perovskite nickelates to the local distribution of hydrogen ions enabled these results. With experimental data from our memory capacitors, simulation results of a reservoir computing framework showed excellent performance for tasks such as digit recognition and classification of electrocardiogram heartbeat activity. Using our reconfigurable artificial neurons and synapses, simulated dynamic networks outperformed static networks for incremental learning scenarios. The ability to fashion the building blocks of brain-inspired computers on demand opens up new directions in adaptive networks. [ABSTRACT FROM AUTHOR]