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Abstract 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.

Additive manufacturing (AM), especially Laser Powder-Bed Fusion (L-PBF), provides alloys with unique properties, but faces printability challenges like porosity and cracks. To address these issues, a co-design strategy integrates chemistry and process indicators to efficiently screen the design space for defect-free combinations. Physics-based models and visualization tools explore the process space, and KGT models guide microstructural design. The approach combines experiments, databases, deep learning models, and Bayesian optimization to streamline AM alloy co-design. By merging computational tools and data-driven techniques with experiments, this integrated approach addresses AM alloy challenges and drives future advancements.

Interlocking metasurfaces (ILMs) are a newly developed joining technology that relies on arrays of interlocking features that transmit force and constrain motion between adjoining bodies in one or more directions. This study explores harnessing the shape memory effect (SME) in Nickel-Titanium shape memory alloys (NiTi SMAs) in structures fabricated using additive manufacturing (AM) to advance the development of active ILMs by creating unit cells that open or close at specific temperatures. The study encompasses designing and fabricating two distinct interlocking array configurations using near-equiatomic NiTi powder and the laser powder bed fusion (L-PBF) AM technique, following a previously developed AM process optimization framework to manufacture defect-free parts. To guide the design process, finite element analysis (FEA) was employed to predict strain values during engage-disengage cycles. The martensitic transformation characteristics of the ILMs were characterized. Thermomechanical testing revealed that the ILMs demonstrate high locking force once engaged, coupled with complete shape recovery and good cyclic stability. Digital image correlation (DIC) was also employed to validate the FEA predictions during the engage-disengage cycles. The results indicate that NiTi SMA-based ILMs can be designed and fabricated into complex shapes using L-PBF. By leveraging the SME, the functionality of an ILM can be improved upon. The combination of computational modeling, additive manufacturing, and thermomechanical and physical property characterization provides a framework for designing future ILMs out of active materials.

The martensitic transformation in NiTi-based Shape Memory Alloys (SMAs) provides a basis for shape memory effect and superelasticity, thereby enabling applications requiring solid-state actuation and large recoverable shape changes upon mechanical load cycling. In order to tailor the transformation to a particular application, the compositional dependence of properties in NiTi-based SMAs, such as martensitic transformation temperatures and hysteresis, has been exploited. However, the compositional design space is large and complex, and experimental studies are expensive. In this work, we develop an interpretable piecewise linear regression model that predicts the λ2 parameter, a measure of compatibility between austenite and martensite phases, and an (indirect) factor that is well-correlated with martensitic transformation hysteresis, based on the chemical features derived from the alloy composition. The model is capable of predicting, for the first time, the type of martensitic transformation for a given alloy chemistry. The proposed model is validated by experimental data from the literature as well as in-house measurements. The results show that the model can effectively distinguish between B19 and B19′ regions for any given composition in NiTi-based SMAs and accurately estimate the λ2 parameter.

Abstract 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 (ΔG form) 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).

Abstract Computational methods are increasingly being incorporated into the exploitation of microstructure–property relationships for microstructure-sensitive design of materials. In the present work, we propose non-intrusive materials informatics methods for the high-throughput exploration and analysis of a synthetic microstructure space using a machine learning-reinforced multi-phase-field modeling scheme. We specifically study the interface energy space as one of the most uncertain inputs in phase-field modeling and its impact on the shape and contact angle of a growing phase during heterogeneous solidification of secondary phase between solid and liquid phases. We evaluate and discuss methods for the study of sensitivity and propagation of uncertainty in these input parameters as reflected on the shape of the Cu6Sn5 intermetallic during growth over the Cu substrate inside the liquid Sn solder due to uncertain interface energies. The sensitivity results rank σ SI ,σ IL , and σ IL , respectively, as the most influential parameters on the shape of the intermetallic. Furthermore, we use variational autoencoder, a deep generative neural network method, and label spreading, a semi-supervised machine learning method for establishing correlations between inputs of outputs of the computational model. We clustered the microstructures into three categories (“wetting”, “dewetting”, and “invariant”) using the label spreading method and compared it with the trend observed in the Young-Laplace equation. On the other hand, a structure map in the interface energy space is developed that shows σ SI and σ SL alter the shape of the intermetallic synchronously where an increase in the latter and decrease in the former changes the shape from dewetting structures to wetting structures. The study shows that the machine learning-reinforced phase-field method is a convenient approach to analyze microstructure design space in the framework of the ICME.

Abstract Bayesian optimization (BO) is an indispensable tool to optimize objective functions that either do not have known functional forms or are expensive to evaluate. Currently, optimal experimental design is always conducted within the workflow of BO leading to more efficient exploration of the design space compared to traditional strategies. This can have a significant impact on modern scientific discovery, in particular autonomous materials discovery, which can be viewed as an optimization problem aimed at looking for the maximum (or minimum) point for the desired materials properties. The performance of BO-based experimental design depends not only on the adopted acquisition function but also on the surrogate models that help to approximate underlying objective functions. In this paper, we propose a fully autonomous experimental design framework that uses more adaptive and flexible Bayesian surrogate models in a BO procedure, namely Bayesian multivariate adaptive regression splines and Bayesian additive regression trees. They can overcome the weaknesses of widely used Gaussian process-based methods when faced with relatively high-dimensional design space or non-smooth patterns of objective functions. Both simulation studies and real-world materials science case studies demonstrate their enhanced search efficiency and robustness.

In computational materials research, uncertainty analysis (more specifically, uncertainty propagation, UP) in the outcomes of model predictions is essential in order to establish confidence in the models as well as to validate them against the ground truth (experiments or higher fidelity simulations). Unfortunately, conventional UP models relying on exhaustive sampling from the distributions of input parameters may be impractical, particularly when the models are computationally expensive. In these cases, investigators must sacrifice accuracy in the propagated uncertainty by down-sampling the input distribution. Recently, a method was developed to correct for these inaccuracies by re-weighing the input distributions to create more statistically representative samples. In this work, the method is applied to computational models for the response of materials under high strain rates. The method is shown to effectively approximate converged output distributions at a lower cost than using conventional sampling approaches.

The desire for increased performance and functionality has introduced additional complexities to the design and fabrication of additively manufactured (AM) parts. However, addressing these needs would require improved control over local properties using in-line feedback from fast-acting low-fidelity models during the fabrication process. In this regard, differential evaporation is an inherent characteristic in metal AM processes, directly influencing local chemistry, material properties, functionality, and performance. In the present work, a differential evaporation model (DEM) is presented for laser powder bed fusion (LPBF) AM to predict and control the effect of evaporation on chemistry and properties on local and part-wide scales. The DEM model is coupled with an analytical thermal model that is calibrated against 51.2 Ni [at%] nickel titanium shape memory alloy (NiTi SMA) single-track experiments and a multi-layer model that accounts for the AM part’s multi-layer design and the inherent melt pool overlap and chemistry propagation. The combined hierarchical model, consisting of the thermal, evaporation, and multi-layer components, is used to predict location-specific chemistry for LBPF AM fabrication of Ni50.8Ti49.2 [at%] SMAs. Model predictions are validated with values obtained from multi-layer experiments on a commercial LPBF system, resulting in a root mean square error (RMSE) of 0.25 Ni [at%] for predicted Ni content. Additionally, martensitic transformation temperature, Ms, is calculated and compared with empirical data, resulting in an RMSE of 18.6 K. A practical account of the cumulative and propagative thermal-induced evaporation effect on location-specific chemistry is made through this linkage of models. Fundamentally, this model chain has also provided a solution to the forward modeling problem, enabling steps to be taken towards resolving the inverse design problem of determining processing parameters based on desired location-specific properties.

Alumina-forming austenitic stainless steels (AFA-SSs) are candidates for high temperature applications to replace conventional stainless steels that form unstable chromium-oxide. In this work, a new AFA-SS composition was designed, through thermodynamic considerations, oxidation model calculations, and Genetic Algorithm-based optimization, which is expected to form alumina scale with only few number of alloying additions. The model incorporates thermodynamic stability of alumina with the kinetics of diffusion of aluminum and oxygen, and then considers the role of chromium as the third element, to predict alumina scale formability. In the present study, this model was utilized to design a new AFA-SS with minimum number of alloying additions. In addition, the oxidation behavior of the designed alloy was investigated at 800 °C to demonstrate its alumina forming capability and compared with a well-known alumina-forming steel. The cross-sections of the scales were studied using scanning electron microscopy and energy dispersive spectroscopy. The results confirmed the formation of the alumina layer in the designed alloy and validated the utility of the model and Genetic Algorithm-based optimization. Keywords: Alumina-forming austenitic stainless steels, Oxidation, Alloy design, Third element effect, Genetic algorithm based optimization

The high-temperature tetragonal phase of HfO2 is technologically useful but difficult to stabilize at room temperature. Here, the authors observe in real-time the transformation of a HfO2nanorod from its room temperature to tetragonal phase, at 1000° less than its bulk temperature, suggesting that size confinement may kinetically trap this phase.

Changes in composition, crystal structure and phase transformations of B2-(Ni,Pt)Al coatings upon isothermal oxidation experiments (natural and scale free oxidation) at 1100 °C, as a function of time beyond their martensitic transformation, are reported. Specifically, the analysis of lattice parameter and composition are performed to identify changes in the B2-(Ni,Pt)Al phase upon the chemically-driven L10-(Ni,Pt)Al and L12-(Ni,Pt)3Al transformations. The B2-(Ni,Pt)Al phase was found to disorder and transform the martensite during the heat treatments for both oxidation experiments at approximately 36.3 and 40.9 at. % of Al, 47.7 and 42.9 at. % of Ni, 6.2 and 8.5 at. % of Pt, 4.2 and 2.9 at. % of Cr and 4.4 and 3.8 at. % of Co. The lattice constant and the long-range order parameter of the B2-(Ni,Pt)Al phase decreased linearly as a function of the elemental content irrespective of the nature of the oxidation experiments.

Quality control in additive manufacturing can be achieved through variation control of the quantity of interest (QoI). We choose in this work the microstructural microsegregation to be our QoI. Microsegregation results from the spatial redistribution of a solute element across the solid–liquid interface that forms during solidification of an alloy melt pool during the laser powder bed fusion process. Since the process as well as the alloy parameters contribute to the statistical variation in microstructural features, uncertainty analysis of the QoI is essential. High-throughput phase-field simulations estimate the solid–liquid interfaces that grow for the melt pool solidification conditions that were estimated from finite element simulations. Microsegregation was determined from the simulated interfaces for different process and alloy parameters. Correlation, regression, and surrogate model analyses were used to quantify the contribution of different sources of uncertainty to the QoI variability. We found negligible contributions of thermal gradient and Gibbs–Thomson coefficient and considerable contributions of solidification velocity, liquid diffusivity, and segregation coefficient on the QoI. Cumulative distribution functions and probability density functions were used to analyze the distribution of the QoI during solidification. Our approach, for the first time, identifies the uncertainty sources and frequency densities of the QoI in the solidification regime relevant to additive manufacturing. [ABSTRACT FROM AUTHOR]