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Machine Learning (ML) potentials such as Gaussian Approximation Potential (GAP) have demonstrated impressive capabilities in mapping structure to properties across diverse systems. Here, we introduce a GAP model for low-dimensional Ni nanoclusters and demonstrate its flexibility and effectiveness in capturing the energetics, structural diversity and thermodynamic properties of Ni nanoclusters across a broad size range. Through a systematic approach encompassing model development, validation, and application, we evaluate the model's efficacy in representing energetics and configurational features in low-dimensional regimes, while also examining its extrapolative nature to vastly different spatiotemporal regimes. Our analysis and discussion shed light on the data quality required to effectively train such models. Trajectories from large scale MD simulations using the GAP model analyzed with data-driven models like Graph Neural Networks (GNN) reveal intriguing insights into the size-dependent phase behavior and thermo-mechanical stability characteristics of porous Ni nanoparticles. Overall, our work underscores the potential of ML models which coupled with data-driven approaches serve as versatile tools for studying low-dimensional systems and complex material dynamics.

Solid-state devices made from correlated oxides such as perovskite nickelates are promising for neuromorphic computing by mimicking biological synaptic function. However, comprehending dopant action at the nanoscale poses a formidable challenge to understanding the elementary mechanisms involved. Here, we perform operando infrared nanoimaging of hydrogen-doped correlated perovskite, neodymium nickel oxide (H-NdNiO3) devices and reveal how an applied field perturbs dopant distribution at the nanoscale. This perturbation leads to stripe phases of varying conductivity perpendicular to the applied field, which define the macroscale electrical characteristics of the devices. Hyperspectral nano-FTIR imaging in conjunction with density functional theory calculations unveil a real-space map of multiple vibrational states of H-NNO associated with OH stretching modes and their dependence on the dopant concentration. Moreover, the localization of excess charges induces an out-of-plane lattice expansion in NNO which was confirmed by in-situ - x-ray diffraction and creates a strain that acts as a barrier against further diffusion. Our results and the techniques presented here hold great potential to the rapidly growing field of memristors and neuromorphic devices wherein nanoscale ion motion is fundamentally responsible for function., Comment: 30 pages, 5 figures in the main text and 5 figures in the Supplementary Material

Fast and accurate prediction of optimal crystal structure, topology, and microstructures is important for accelerating the design and discovery of new materials. A challenge lies in the exorbitantly large structural and compositional space presented by the various elements and their combinations. Speed, accuracy, and scalability are three desirables for any inverse design tool to sample efficiently across such a vast space. While traditional global optimization approaches (e.g., evolutionary algorithm, random sampling based) have demonstrated the ability to predict new crystal structures that can be used as super-hard materials, semiconductors, and photovoltaic materials to name a few, it is highly desirable to develop approaches that converge faster to the solution, have better solution quality, and are scalable to high dimensionality. Reinforcement learning (RL) approaches are emerging as powerful design tools capable of addressing these issues but primarily operate in discrete action space. In this work, we introduce CASTING, which is an RL-based scalable framework for crystal structure, topology, and potentially microstructure prediction. CASTING employs an RL-based continuous search space decision tree (MCTS -Monte Carlo Tree Search) algorithm with three important modifications (i) a modified rewards scheme for improved search space exploration (ii) a 'windowing' or 'funneling' scheme for improved exploitation and (iii) adaptive sampling during playouts for efficient and scalable search. Using a set of representative examples ranging from metals such as Ag to covalent systems such as C and multicomponent systems (graphane, boron nitride, and complex correlated oxides), we demonstrate the accuracy, the speed of convergence, and the scalability of CASTING to discover new metastable crystal structures and phases that meet the target objective.

Probabilistic computing has emerged as a viable approach to solve hard optimization problems. Devices with inherent stochasticity can greatly simplify their implementation in electronic hardware. Here, we demonstrate intrinsic stochastic resistance switching controlled via electric fields in perovskite nickelates doped with hydrogen. The ability of hydrogen ions to reside in various metastable configurations in the lattice leads to a distribution of transport gaps. With experimentally characterized p-bits, a shared-synapse p-bit architecture demonstrates highly-parallelized and energy-efficient solutions to optimization problems such as integer factorization and Boolean-satisfiability. The results introduce perovskite nickelates as scalable potential candidates for probabilistic computing and showcase the potential of light-element dopants in next-generation correlated semiconductors.

Machine learning models and applications in materials design and discovery typically involve the use of feature representations or "descriptors" followed by a learning algorithm that maps them to a user-desired property of interest. Most popular mathematical formulation-based descriptors are not unique across atomic environments or suffer from transferability issues across different application domains and/or material classes. In this work, we introduce the Crystal Edge Graph Attention Neural Network (CEGANN) workflow that uses graph attention-based architecture to learn unique feature representations and perform classification of materials across multiple scales (from atomic to mesoscale) and diverse classes ranging from metals, oxides, non-metals and even hierarchical materials such as zeolites and semi ordered materials such as mesophases. We first demonstrate a case study where the classification is based on a global, structure-level representation such as space group and structural dimensionality (e.g., bulk, 2D, clusters etc.). Using representative materials such as polycrystals and zeolites, we next demonstrate the transferability of our network in successfully performing local atom-level classification tasks, such as grain boundary identification and other heterointerfaces. We also demonstrate classification in (thermal) noisy dynamical environments using a representative example of crystal nucleation and growth of a zeolite polymorph from an amorphous synthesis mixture. Finally, we characterize the formation of a binary mesophase and its phase transitions and the growth of ice, demonstrating the performance of CEGANN in systems with thermal noise and compositional diversity. Overall, our approach is agnostic to the material type and allows for multiscale classification of features ranging from atomic-scale crystal structures to heterointerfaces to microscale grain boundaries.

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. 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 of training data to ensure that the model adequately samples the energy landscape both near and far-from-equilibrium. 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. Our NN predictions are within 30 meV/atom and 40 meV/\AA 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.

Phase diagrams are an invaluable tool for material synthesis and provide information on the phases of the material at any given thermodynamic condition. Conventional phase diagram generation involves experimentation to provide an initial estimate of thermodynamically accessible phases, followed by use of phenomenological models to interpolate between the available experimental data points and extrapolate to inaccessible regions. Such an approach, combined with first-principles calculations and data-mining techniques, has led to exhaustive thermodynamic databases albeit at distinct thermodynamic equilibria. In contrast, materials during their synthesis, operation, or processing, may not reach their thermodynamic equilibrium state but, instead, remain trapped in a local free energy minimum, that may exhibit desirable properties. Mapping these metastable phases and their thermodynamic behavior is highly desirable but currently lacking. Here, we introduce an automated workflow that integrates first principles physics and atomistic simulations with machine learning (ML), and high-performance computing to allow rapid exploration of the metastable phases of a given elemental composition. Using a representative material, carbon, with a vast number of metastable phases without parent in equilibrium, we demonstrate automatic mapping of hundreds of metastable states ranging from near equilibrium to those far-from-equilibrium. Moreover, we incorporate the free energy calculations into a neural-network-based learning of the equations of state that allows for construction of metastable phase diagrams. High temperature high pressure experiments using a diamond anvil cell on graphite sample coupled with high-resolution transmission electron microscopy are used to validate our metastable phase predictions. Our introduced approach is general and broadly applicable to single and multi-component systems., Comment: 32 pages 16 figures; v3: Added acknowledgments and removed affiliations from author metadata

In this work, we expand the set of known layered compounds to include ionic layered materials, which are well known for superconducting, thermoelectric, and battery applications. Focusing on known ternary compounds from the ICSD, we screen for ionic layered structures by expanding upon our previously developed algorithm for identification of van der Waals (vdW) layered structures, thus identifying over 1,500 ionic layered compounds. Since vdW layered structures can be chemically mutated to form ionic layered structures, we have developed a methodology to structurally link binary vdW to ternary ionic layered materials. We perform an in-depth analysis of similarities and differences between these two classes of layered systems and assess the interplay between layer geometry and bond strength with a study of the elastic anisotropy. We observe a rich variety of anisotropic behavior in which the layering direction alone is not a simple predictor of elastic anisotropy. Our results enable discovery of new layered materials through intercalation or de- intercalation of vdW or ionic layered systems, respectively, as well as lay the groundwork for studies of anisotropic transport phenomena such as sound propagation or lattice thermal conductivity.

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 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.

The bulk piezoelectric response, as measured by the piezoelectric modulus tensor (\textbf{d}), is determined by a combination of charge redistribution due to strain and the amount of strain produced by the application of stress (stiffness). Motivated by the notion that less stiff materials could exhibit large piezoelectric responses, herein we investigate the piezoelectric modulus of van der Waals-bonded quasi-2D ionic compounds using first-principles calculations. From a pool of 869 known binary and ternary quasi-2D materials, we have identified 135 non-centrosymmetric crystals of which 48 systems are found to have \textbf{d} components larger than the longitudinal piezoelectric modulus of AlN (a common piezoelectric for resonators), and three systems with the response greater than that of PbTiO$_3$, which is among the materials with largest known piezoelectric modulus. None of the identified materials have previously been considered for piezoelectric applications. Furthermore, we find that large \textbf{d} components always couple to the deformations (shearing or axial) of van der Waals "gaps" between the layers and are indeed enabled by the weak intra-layer interactions.

Certain alumino-silicates display exotic properties enabled by their framework structure made of corner-sharing tetrahedral rigid units. Using \textit{in situ} diamond-anvil cell x-ray diffraction (XRD), we study the pressure-induced transformation of $\beta$ eucryptite, a prototypical alumino-silicate. $\beta$ eucryptite undergoes a phase transformation at moderate pressures, but the atomic structure of the new phase has not yet been reported. Based on density functional theory stability studies and Rietveld analysis of XRD patterns, we find that the pressure-stabilized phase belongs to the Pna2$_1$ space group. Furthermore, we discover two other possible pressure-stabilized polymorphs, P1c1 and Pca2$_1$.

Since AlN has emerged as an important piezoelectric material for a wide variety of applications, efforts have been made to increase its piezoelectric response via alloying with transition metals that can substitute for Al in the wurtzite lattice. Herein, we report density functional theory calculations of structure and properties of the Cr-AlN system for Cr concentrations ranging past the wurtzite-rocksalt transition point. By studying the different contributions to the longitudinal piezoelectric coefficient, we propose that the physical origin of the enhanced piezoelectricity in Cr$_x$Al$_{1-x}$N alloys is the increase of the internal parameter $u$ of the wurtzite structure upon substitution of Al with the larger Cr ions. Among a set of wurtzite-structured materials, we have found that Cr-AlN has the most sensitive piezoelectric coefficient with respect to alloying concentration. Based on these results, we propose that Cr-AlN is a viable piezoelectric material whose properties can be tuned via Cr composition; we support this proposal by combinatorial synthesis experiments, which show that Cr can be incorporated in the AlN lattice up to 30\% before a detectable transition to rocksalt occurs. At this Cr content, the piezoelectric modulus $d_{33}$ is approximately four times larger than that of pure AlN. This finding, combined with the relative ease of synthesis, may propel Cr-AlN as the prime piezoelectric material for applications such as resonators and acoustic wave generators., Comment: Submitted to Physical Review Applied

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 electromechanical 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 a softened lattice. We show that co-alloying of AlN with YN and BN results in improved elastic properties while retaining most 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. Keywords: piezoelectricity, electromechanical coupling, density functional theory, co-alloying, Comment: submitted to Journal of Applied Physics

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 -eucryptite, which is a prototypical lithium aluminosilicate. In the absence of significant structural transitions in grain boundaries, we have found that there are three conduction regimes for the one-dimensional ionic motion along the c axis channels in the grains, and determined 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 {\deg}C conduction proceeds via cooperative or correlated motion, in agreement with established literature. Between 440 {\deg}C and 500{\deg}C, the activation barriers extracted from EIS measurements are large and consistent with those from atomistic calculations for uncorrelated Li ion hopping. Above 500 {\deg}C the activation barriers decrease significantly, which indicates that after the transition to the Li-disordered phase of -eucryptite, the Li ion motion largely regains the correlated character., Comment: submitted to Journal of American Ceramic Society

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.