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Phase diagrams supported by density functional theory methods can be crucial for designing high-entropy alloys that are subset of multi-principal-element alloys. We present phase and property analysis of quinary (MoW)xZry(TaTi)1-x-y refractory high-entropy alloys from combined Calculation of Phase Diagram (CALPHAD) and density-functional theory results, supplemented by molecular dynamics simulations. Both CALPHAD and density-functional theory analysis of phase stability indicates a Mo-W-rich region of this quinary has a stable single-phase body-centered-cubic structure. We report first quinary composition from Mo-W-Ta-Ti-Zr family of alloy with pseudo-elastic behavior, i.e., hysteresis in stress-strain. Our analysis shows that only Mo-W-rich compositions of Mo-W-Ta-Ti-Zr, i.e., Mo+W ≥ 85 at.%, show reproducible hysteresis in stress-strain responsible for pseudo-elastic behavior. The (MoW)85Zr7.5(TaTi)7.5 was down-selected based on temperature-dependent phase diagram analysis and molecular dynamics simulations predicted elastic behavior that reveals twinning-assisted pseudoelastic behavior. While mostly unexplored in body-centered-cubic crystals, twinning is a fundamental deformation mechanism that competes against dislocation slip in crystalline solids. This alloy shows identical cyclic deformation characteristics during uniaxial loading, i.e., the pseudoelasticity is isotropic in loading direction. Additionally, a temperature increase from 77 to 1,500 K enhances the elastic strain recovery in load-unload cycles, offering possibly control to tune the pseudoelastic behavior.

High Entropy Alloys (HEAs) are an increasingly dominant alloy design paradigm. The premise of entropic stabilization of single-phase alloys has motivated much of the research on HEAs. Chemical complexity may indeed help stabilize single alloy phases relative to other lower-entropy competing solid phases. Paradoxically, this complexity may de-stabilize these alloys against the liquid, potentially limiting the application space of HEAs at elevated temperatures. In this work, we carry out a comprehensive investigation of the phase stability in the fcc CoCrFeMnNiVAl HEA space using a state of the art CALPHAD database. By using modern

Traditional microstructure classification requires human annotations provided by a subject matter expert. The requirement of human input is both costly and subjective and cannot keep up with the current volume of experimentally and computationally generated microstructure images. In this work, we develop a framework that is capable of reducing the cost of human annotation in this process by leveraging novel machine learning procedures for class discovery and label assignment. To reduce the penalty of a poor label assignment made by this automated process, labels are only assigned to high-confidence observations while ambiguous data are left unlabeled. Semi-supervised classification is then employed to leverage the high- and low-confidence label assignments, and a novel generalization of an established semi-supervised error estimation technique to the multi-class context is introduced to assess the resulting classifiers. Finally, it is shown that this framework can be used to produce highly accurate classifiers over microstructure image class taxonomies which are discovered solely through data-driven methods and which display consistent structural trends within and distinct morphological differences between classes.

We present a general framework for the design/optimization of materials that is capable of accounting for multiple information sources available to the materials designer. We demonstrate the framework through the microstructure-based design of multi-phase microstructures. Specifically, we seek to maximize the strength normalized strain-hardening rate of a dual-phase ferritic/martensitic steel through a multi-information source Bayesian optimal design strategy. We assume that we have multiple sources of information with varying degrees of fidelity as well as cost. The available information from all sources is fused through a reification approach and then a sequential experimental design is carried out. The experimental design seeks not only to identify the most promising region in the materials design space relative to the objective at hand, but also to identify the source of information that should be used to query this point in the decision space. The selection criterion for the source used accounts for the discrepancy between the source and the 'ground truth' as well as its cost. It is shown that when there is a hard constraint on the budget available to carry out the optimization, accounting for the cost of querying individual sources is essential.

ICME approaches provide decision support for materials design by establishing quantitative process-structure-property relations. Confidence in the decision support, however, must be achieved by establishing uncertainty bounds in ICME model chains. The quantification and propagation of uncertainty in computational materials science, however, remains a rather unexplored aspect of computational materials science approaches. Moreover, traditional uncertainty propagation frameworks tend to be limited in cases with computationally expensive simulations. A rather common and important model chain is that of CALPHAD-based thermodynamic models of phase stability coupled to phase field models for microstructure evolution. Propagation of uncertainty in these cases is challenging not only due to the sheer computational cost of the simulations but also because of the high dimensionality of the input space. In this work, we present a framework for the quantification and propagation of uncertainty in a CALPHAD-based elasto-chemical phase field model. We motivate our work by investigating the microstructure evolution in Mg$_2$(Si$_x$Sn$_{1-x}$) thermoelectric materials. We first carry out a Markov Chain Monte Carlo-based inference of the CALPHAD model parameters for this pseudobinary system and then use advanced sampling schemes to propagate uncertainties across a high-dimensional simulation input space. Through high-throughput phase field simulations we generate 200,000 time series of synthetic microstructures and use machine learning approaches to understand the effects of propagated uncertainties on the microstructure landscape of the system under study. The microstructure dataset has been curated in the Open Phase-field Microstructure Database (OPMD), available at \href{http://microstructures.net}{http://microstructures.net}.

We propose a methodology for predicting the printability of an alloy, subject to laser powder bed fusion additive manufacturing. Regions in the process space associated with keyhole formation, balling, and lack of fusion are assumed to be strong functions of the geometry of the melt pool, which in turn is calculated for various combinations of laser power and scan speed via a Finite Element thermal model that incorporates a novel vaporization-based transition from surface to volumetric heating upon keyhole formation. Process maps established from the Finite Element simulations agree with experiments for a Ni-5wt.%Nb alloy and an equiatomic CoCrFeMnNi High Entropy Alloy and suggest a strong effect of chemistry on alloy printability. The printability maps resulting from the use of the simpler Eagar-Tsai model, on the other hand, are found to be in disagreement with experiments due to the oversimplification of this approach. Uncertainties in the printability maps were quantified via Monte Carlo sampling of a multivariate Gaussian Processes surrogate model trained on simulation outputs. The printability maps generated with the proposed method can be used in the selection --- and potentially the design --- of alloys best suited for Additive Manufacturing.

MAX phases have attracted increased attention due to their unique combination of ceramic and metallic properties. Point-defects are known to play vital role in the structural, electronic and transport properties of alloys. However, the effects of disorder are not well explored, despite being a key player in this context. This work investigates the alloying effect on structural-stability, energy-stability, electronic-structure, and diffusion barrier of point defects in MAX phase alloys within first-principles density functional framework. The vacancy (VM, VA, VX) and antisite (M-A; M-X) defects are considered with M and A site disorder in (Zr-M)2(AA')C, where M=Cr,Nb,Ti and AA'=Al, Al-Sn, Pb-Bi. Our calculations suggest that the chemical disorder helps lower the formation energies of VA compared to VM and VX. The VA diffusion barrier is also significantly reduced for M-site disorder compared to their ordered counterpart. We believe that our study will provide a fundamental understanding and an approach to tailor the key properties that can lead to the discovery of new MAX phase alloys.

We present a computational-experimental study on the ternary alloying effect of CuZr-based shape memory alloy. The transformation behavior, including crystallization, martensite-austenite transformation temperature and hysteresis of Cu-Zr-X (X = Ni, Co, Hf) thin-film samples were investigated by nanocalorimetry. We used ab initio simulations to determine the B2-Cm transformation pathway, evaluate the lattice parameters, the relative phase stability, and the twin boundary energy as a function of composition. Experimental results show that alloying with Ni or Co reduces the hysteresis of the martensitic transformation, while Hf increases it. These observations are in agreement with the trend of the middle eigenvalue of the martensitic transformation matrix. The energy difference between the pure phases obtained from simulations suggests that both Co and Ni stabilize martensite against austenite. However, experiments show that Co decreases transformation temperature, while Ni increases it. We attribute this observation to the larger twin boundary energy and strain energy in the Co-containing alloy. Our results indicate that ab initio simulations are a helpful tool in the development of new shape memory alloys, provided the energy terms associated with the fine twin structure of the martensite are taken into account.

Ti$_{1-x-y}$Al$_{x}$Zr$_{y}$N cubic alloys within the 25-70\% Al composition range have high age-hardening capabilities due to metastable phase transition pathways at high temperatures. They are thus ideal candidates for ultra-hard nano-coating materials. There is growing evidence that this effect is associated with the elasto-chemical field-induced phase separation into compositionally-segregated nanocrystaline nitride phases. Here, we studied the microstructural evolution in this pseudo-ternary system within spinodal regions at 1200 $^\circ$C by using an elasto-chemical phase field model. Our simulations indicate that elastic interactions between nitride nano-domains greatly affect not only the morphology of the microstructure but also the local chemical phase equilibria. In Al-rich regions of the composition space we further observe the onset of the transformation of AlN-rich phases into their equilibrium wurtzite crystal structure. This work points to a wide palette of microstructures potentially accessible to these nitride systems and their tailoring is likely to result in significant improvements in the performance of transition metal nitride-based coating materials.