Hybrid magnon-phonon localization enhances function near ferroic glassy states.

Ferroic materials on the verge of forming ferroic glasses exhibit heightened functionality that is often attributed to competing long- and short-range correlations. However, the physics underlying these enhancements is not well understood. The Ni45Co5Mn36.6In13.4 Heusler alloy is on the edge of forming both spin and strain glasses and exhibits magnetic field-induced shape memory and large magnetocaloric effects, making it a candidate for multicaloric cooling applications. We show using neutron scattering that localized magnon-phonon hybrid modes, which are inherently spread across reciprocal space, act as a bridge between phonons and magnons and result in substantial magnetic field-induced shifts in the phonons, triple the caloric response, and alter phase stability. We attribute these modes to the localization of phonons and magnons by antiphase boundaries coupled to magnetic domains. Because the interplay between short- and long-range correlations is common near ferroic glassy states, our work provides general insights on how glassiness enhances function.

True random number generation using the spin crossover in LaCoO3.

While digital computers rely on software-generated pseudo-random number generators, hardware-based true random number generators (TRNGs), which employ the natural physics of the underlying hardware, provide true stochasticity, and power and area efficiency. Research into TRNGs has extensively relied on the unpredictability in phase transitions, but such phase transitions are difficult to control given their often abrupt and narrow parameter ranges (e.g., occurring in a small temperature window). Here we demonstrate a TRNG based on self-oscillations in LaCoO3 that is electrically biased within its spin crossover regime. The LaCoO3 TRNG passes all standard tests of true stochasticity and uses only half the number of components compared to prior TRNGs. Assisted by phase field modeling, we show how spin crossovers are fundamentally better in producing true stochasticity compared to traditional phase transitions. As a validation, by probabilistically solving the NP-hard max-cut problem in a memristor crossbar array using our TRNG as a source of the required stochasticity, we demonstrate solution quality exceeding that using software-generated randomness.

Knowledge-driven learning, optimization, and experimental design under uncertainty for materials discovery.

Significant acceleration of the future discovery of novel functional materials requires a fundamental shift from the current materials discovery practice, which is heavily dependent on trial-and-error campaigns and high-throughput screening, to one that builds on knowledge-driven advanced informatics techniques enabled by the latest advances in signal processing and machine learning. In this review, we discuss the major research issues that need to be addressed to expedite this transformation along with the salient challenges involved. We especially focus on Bayesian signal processing and machine learning schemes that are uncertainty aware and physics informed for knowledge-driven learning, robust optimization, and efficient objective-driven experimental design.

Deep learning-accelerated computational framework based on Physics Informed Neural Network for the solution of linear elasticity.

The paper presents an efficient and robust data-driven deep learning (DL) computational framework developed for linear continuum elasticity problems. The methodology is based on the fundamentals of the Physics Informed Neural Networks (PINNs). For an accurate representation of the field variables, a multi-objective loss function is proposed. It consists of terms corresponding to the residual of the governing partial differential equations (PDE), constitutive relations derived from the governing physics, various boundary conditions, and data-driven physical knowledge fitting terms across randomly selected collocation points in the problem domain. To this end, multiple densely connected independent artificial neural networks (ANNs), each approximating a field variable, are trained to obtain accurate solutions. Several benchmark problems including the Airy solution to elasticity and the Kirchhoff-Love plate problem are solved. Performance in terms of accuracy and robustness illustrates the superiority of the current framework showing excellent agreement with analytical solutions. The present work combines the benefits of the classical methods depending on the physical information available in analytical relations with the superior capabilities of the DL techniques in the data-driven construction of lightweight, yet accurate and robust neural networks. The models developed herein can significantly boost computational speed using minimal network parameters with easy adaptability in different computational platforms.

A theoretical investigation of the effect of Ga alloying on thermodynamic stability, electronic-structure, and oxidation resistance of Ti2AlC MAX phase.

We present a systematic investigation of thermodynamic stability, phase-reaction, and chemical activity of Al containing disordered Ti2(Al-Ga)C MAX phases using machine-learning driven high-throughput framework to understand the oxidation resistance behavior with increasing temperature and exposure to static oxygen. The A-site (at Al) disordering in  Ti2AlC MAX (M=Ti, A=Al, X=C) with Ga shows significant change in the chemical activity of Al with increasing temperature and exposure to static oxygen, which is expected to enable surface segregation of Al, thereby, the formation of Al2O3 and improved oxidation resistance. We performed in-depth convex hull analysis of ternary Ti-Al-C, Ti-Ga-C, and Ti-Al-Ga-C based MAX phase, and provide detailed contribution arising from electronic, chemical and vibrational entropies. The thermodynamic analysis shows change in the Gibbs formation enthalpy (ΔGform) at higher temperatures, which implies an interplay of temperature-dependent enthalpy and entropic contributions in oxidation resistance Ga doped Ti2AlC MAX phases. A detailed electronic structure and chemical bonding analysis using crystal orbital Hamilton population method reveal the origin of change in phases stability and in oxidation resistance in disorder Ti2(Al1-xGax)C MAX phases. Our electronic structure analysis correlate well with the change in oxidation resistance of Ga doped MAX phases. We believe our study provides a useful guideline to understand to role of alloying on electronic, thermodynamic, and oxidation related mechanisms of bulk MAX phases, which can work as a precursor to understand oxidation behavior of two-dimensional MAX phases, i.e., MXenes (transition metal carbides, carbonitrides and nitrides).

Predicting Van der Waals Heterostructures by a Combined Machine Learning and Density Functional Theory Approach.

Van der Waals (vdW) heterostructures are constructed by different two-dimensional (2D) monolayers vertically stacked and weakly coupled by van der Waals interactions. VdW heterostructures often possess rich physical and chemical properties that are unique to their constituent monolayers. As many 2D materials have been recently identified, the combinatorial configuration space of vdW-stacked heterostructures grows exceedingly large, making it difficult to explore through traditional experimental or computational approaches in a trial-and-error manner. Here, we present a computational framework that combines first-principles electronic structure calculations, 2D material database, and supervised machine learning methods to construct efficient data-driven models capable of predicting electronic and structural properties of vdW heterostructures from their constituent monolayer properties. We apply this approach to predict the band gap, band edges, interlayer distance, and interlayer binding energy of vdW heterostructures. Our data-driven model will open avenues for efficient screening and discovery of low-dimensional vdW heterostructures and moiré superlattices with desired electronic and optical properties for targeted device applications.

Incommensurate transition-metal dichalcogenides via mechanochemical reshuffling of binary precursors.

A new family of heterostructured transition-metal dichalcogenides (TMDCs) with incommensurate ("misfit") spatial arrangements of well-defined layers was prepared from structurally dissimilar single-phase 2H-MoS2 and 1T-HfS2 materials. The experimentally observed heterostructuring is energetically favorable over the formation of homogeneous multi-principle element dichalcogenides observed in related dichalcogenide systems of Mo, W, and Ta. The resulting three-dimensional (3D) heterostructures show semiconducting behavior with an indirect band gap around 1 eV, agreeing with values predicted from density functional theory. Results of this joint experimental and theoretical study open new avenues for generating unexplored metal-dichalcogenide heteroassemblies with incommensurate structures and tunable physical properties.

Effect of Pd alloying on structural, electronic and magnetic properties of L10Fe-Ni.

We present a systematic study of the effect of Pd-alloying on phase stability, electronic structure, and elastic properties in L10Fe-Ni using density-functional theory. Being from the same group of the periodic table, Pd is the best candidate for chemical alloying. The Fe-Ni/Fe-Pd/Ni-Pd bond-length increases with increasing Pd-concentration, which weakens the hybridization between low lying energy states below Fermi-level. The reduced hybridization decreases the relative thermodynamic stability of L10Fe(Ni1-xPdx) untilx= 0.75. Beyond this concentration, the relative stability gets enhanced, which is attributed to a unique change in the lattice distortion (c/a). The elastic properties show a non-monotonous behavior as a function ofx, which is again due to a specific change-over in the uniaxial strain. We found that Pd alloying increases the local Fe moment and structural anisotropy of L10FeNi, which are important for applications such as microwave absorption, refrigeration systems, recording devices, imaging and sensors. We believe that the present study for the chemical alloying effect can provide critical insights toward the understanding of electronic-structure and elastic behavior of other technologically important materials.

Atomic Hourglass and Thermometer Based on Diffusion of a Mobile Dopant in VO2.

Transformations between different atomic configurations of a material oftentimes bring about dramatic changes in functional properties as a result of the simultaneous alteration of both atomistic and electronic structure. Transformation barriers between polytypes can be tuned through compositional modification, generally in an immutable manner. Continuous, stimulus-driven modulation of phase stabilities remains a significant challenge. Utilizing the metal-insulator transition of VO2, we exemplify that mobile dopants weakly coupled to the crystal lattice provide a means of imbuing a reversible and dynamical modulation of the phase transformation. Remarkably, we observe a time- and temperature-dependent evolution of the relative phase stabilities of the M1 and R phases of VO2 in an "hourglass" fashion through the relaxation of interstitial boron species, corresponding to a 50 °C modulation of the transition temperature achieved within the same compound. The material functions as both a chronometer and a thermometer and is "reset" by the phase transition. Materials possessing memory of thermal history hold promise for applications such as neuromorphic computing, atomic clocks, thermometry, and sensing.

Navigating the design space of inorganic materials synthesis using statistical methods and machine learning.

Data-driven approaches have brought about a revolution in manufacturing; however, challenges persist in their applications to synthetic strategies. Their application to the deterministic navigation of reaction trajectories to stabilize crystalline solids with precise composition, atomic connectivity, microstructural dimensionality, and surface structure remains a frontier in inorganic materials research. The design of synthetic methodologies for the preparation of inorganic materials is often inefficient in terms of exploration of potentially vast design spaces spanning multiple process variables, reaction sequences, as well as structural parameters and reactivities of precursors and structure-directing agents. Reported synthetic methods are further limited in terms of the insight they provide into underlying chemical and physical principles. The recent surge in interest in accelerating the discovery of new materials can be considered as an opportunity to re-evaluate our approach to materials synthesis, and for considering new frameworks for exploration that are systematic and strategic in approach. Herein, we outline with the help of several illustrative examples, the challenges, opportunities, and limitations of data-driven synthesis design. The account collates discussion of design-of-experiments sampling methods, machine learning modeling, and active learning to develop experimental workflows that accelerate the experimental navigation of synthetic landscapes.

Impact of particle arrays on phase separation composition patterns.

We examine the symmetry-breaking effect of fixed constellations of particles on the surface-directed spinodal decomposition of binary blends in the presence of particles whose surfaces have a preferential affinity for one of the components. Our phase-field simulations indicate that the phase separation morphology in the presence of particle arrays can be tuned to have a continuous, droplet, lamellar, or hybrid morphology depending on the interparticle spacing, blend composition, and time. In particular, when the interparticle spacing is large compared to the spinodal wavelength, a transient target pattern composed of alternate rings of preferred and non-preferred phases emerges at early times, tending to adopt the symmetry of the particle configuration. We reveal that such target patterns stabilize for certain characteristic length, time, and composition scales characteristic of the pure phase-separating mixture. To illustrate the general range of phenomena exhibited by mixture-particle systems, we simulate the effects of single-particle, multi-particle, and cluster-particle systems having multiple geometrical configurations of the particle characteristic of pattern substrates on phase separation. Our simulations show that tailoring the particle configuration, or substrate pattern configuration, a relative fluid-particle composition should allow the desirable control of the phase separation morphology as in block copolymer materials, but where the scales accessible to this approach of organizing phase-separated fluids usually are significantly larger. Limited experiments confirm the trends observed in our simulations, which should provide some guidance in engineering patterned blend and other mixtures of technological interest.

On the stochastic phase stability of Ti2AlC-Cr2AlC.

The quest towards expansion of the M n+1AX n design space has been accelerated with the recent discovery of several solid solution and ordered phases involving at least two M n+1AX n end members. Going beyond the nominal M n+1AX n compounds enables not only fine tuning of existing properties but also entirely new functionality. This search, however, has been mostly done through painstaking experiments as knowledge of the phase stability of the relevant systems is rather scarce. In this work, we report the first attempt to evaluate the finite-temperature pseudo-binary phase diagram of the Ti2AlC-Cr2AlC via first-principles-guided Bayesian CALPHAD framework that accounts for uncertainties not only in ab initio calculations and thermodynamic models but also in synthesis conditions in reported experiments. The phase stability analyses are shown to have good agreement with previous experiments. The work points towards a promising way of investigating phase stability in other MAX Phase systems providing the knowledge necessary to elucidate possible synthesis routes for M n+1AX n systems with unprecedented properties.

Real-time atomistic observation of structural phase transformations in individual hafnia nanorods.

High-temperature phases of hafnium dioxide have exceptionally high dielectric constants and large bandgaps, but quenching them to room temperature remains a challenge. Scaling the bulk form to nanocrystals, while successful in stabilizing the tetragonal phase of isomorphous ZrO2, has produced nanorods with a twinned version of the room temperature monoclinic phase in HfO2. Here we use in situ heating in a scanning transmission electron microscope to observe the transformation of an HfO2 nanorod from monoclinic to tetragonal, with a transformation temperature suppressed by over 1000°C from bulk. When the nanorod is annealed, we observe with atomic-scale resolution the transformation from twinned-monoclinic to tetragonal, starting at a twin boundary and propagating via coherent transformation dislocation; the nanorod is reduced to hafnium on cooling. Unlike the bulk displacive transition, nanoscale size-confinement enables us to manipulate the transformation mechanism, and we observe discrete nucleation events and sigmoidal nucleation and growth kinetics.

Spatial Control of Functional Response in 4D-Printed Active Metallic Structures.

We demonstrate a method to achieve local control of 3-dimensional thermal history in a metallic alloy, which resulted in designed spatial variations in its functional response. A nickel-titanium shape memory alloy part was created with multiple shape-recovery stages activated at different temperatures using the selective laser melting technique. The multi-stage transformation originates from differences in thermal history, and thus the precipitate structure, at various locations created from controlled variations in the hatch distance within the same part. This is a first example of precision location-dependent control of thermal history in alloys beyond the surface, and utilizes additive manufacturing techniques as a tool to create materials with novel functional response that is difficult to achieve through conventional methods.

Correction: Phase stability in nanoscale material systems: extension from bulk phase diagrams.

Correction for 'Phase stability in nanoscale material systems: extension from bulk phase diagrams' by Saurabh Bajaj et al., Nanoscale, 2015, 7, 9868-9877.

Phase stability in nanoscale material systems: extension from bulk phase diagrams.

Phase diagrams of multi-component systems are critical for the development and engineering of material alloys for all technological applications. At nano dimensions, surfaces (and interfaces) play a significant role in changing equilibrium thermodynamics and phase stability. In this work, it is shown that these surfaces at small dimensions affect the relative equilibrium thermodynamics of the different phases. The CALPHAD approach for material surfaces (also termed "nano-CALPHAD") is employed to investigate these changes in three binary systems by calculating their phase diagrams at nano dimensions and comparing them with their bulk counterparts. The surface energy contribution, which is the dominant factor in causing these changes, is evaluated using the spherical particle approximation. It is first validated with the Au-Si system for which experimental data on phase stability of spherical nano-sized particles is available, and then extended to calculate phase diagrams of similarly sized particles of Ge-Si and Al-Cu. Additionally, the surface energies of the associated compounds are calculated using DFT, and integrated into the thermodynamic model of the respective binary systems. In this work we found changes in miscibilities, reaction compositions of about 5 at%, and solubility temperatures ranging from 100-200 K for particles of sizes 5 nm, indicating the importance of phase equilibrium analysis at nano dimensions.