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In this study, we demonstrate how the incorporation of appropriate feature engineering together with the selection of a Machine Learning (ML) algorithm that best suits the available dataset, leads to the development of a predictive model for transformation temperatures that can be applied to a wide range of shape memory alloys. We develop a gradient boosting ML surrogate model capable of predicting Martensite Start, Martensite Finish, Austenite Start, and Austenite Finish transformation temperatures with an average accuracy of more than 95% by explicitly taking care of potential distribution changes when modeling different alloy systems. We included heat treatment, rolling, extrusion processing parameters, and alloy system categorical features in the model input features to achieve more accurate and realistic results. In addition, using Shapley values, which are calculated based on the average marginal contribution of features to all possible coalitions, this study was able to gain insights into the governing features and their effect on predicted transformation temperatures, providing a unique opportunity to examine the critical parameters and features in martensite transformation temperatures.

Designing alloys for additive manufacturing (AM) presents significant opportunities. Still, the chemical composition and processing conditions required for printability (ie., their suitability for fabrication via AM) are challenging to explore using solely experimental means. In this work, we develop a high-throughput (HTP) computational framework to guide the search for highly printable alloys and appropriate processing parameters. The framework uses material properties from state-of-the-art databases, processing parameters, and simulated melt pool profiles to predict process-induced defects, such as lack-of-fusion, keyholing, and balling. We accelerate the printability assessment using a deep learning surrogate for a thermal model, enabling a 1,000-fold acceleration in assessing the printability of a given alloy at no loss in accuracy when compared with conventional physics-based thermal models. We verify and validate the framework by constructing printability maps for the CoCrFeMnNi Cantor alloy system and comparing our predictions to an exhaustive 'in-house' database. The framework enables the systematic investigation of the printability of a wide range of alloys in the broader Co-Cr-Fe-Mn-Ni HEA system. We identified the most promising alloys that were suitable for high-temperature applications and had the narrowest solidification ranges, and that was the least susceptible to balling, hot-cracking, and the formation of macroscopic printing defects. A new metric for the global printability of an alloy is constructed and is further used for the ranking of candidate alloys. The proposed framework is expected to be integrated into ICME approaches to accelerate the discovery and optimization of novel high-performance, printable alloys., Comment: 46 pages, 20 figures

Purpose There is recent emphasis on designing new materials and alloys specifically for metal additive manufacturing (AM) processes, in contrast to AM of existing alloys that were developed for other traditional manufacturing methods involving considerably different physics. Process optimization to determine processing recipes for newly developed materials is expensive and time-consuming. The purpose of the current work is to use a systematic printability assessment framework developed by the co-authors to determine windows of processing parameters to print defect-free parts from a binary nickel-niobium alloy (NiNb5) using laser powder bed fusion (LPBF) metal AM. Design/methodology/approach The printability assessment framework integrates analytical thermal modeling, uncertainty quantification and experimental characterization to determine processing windows for NiNb5 in an accelerated fashion. Test coupons and mechanical test samples were fabricated on a ProX 200 commercial LPBF system. A series of density, microstructure and mechanical property characterization was conducted to validate the proposed framework. Findings Near fully-dense parts with more than 99% density were successfully printed using the proposed framework. Furthermore, the mechanical properties of as-printed parts showed low variability, good tensile strength of up to 662 MPa and tensile ductility 51% higher than what has been reported in the literature. Originality/value Although many literature studies investigate process optimization for metal AM, there is a lack of a systematic printability assessment framework to determine manufacturing process parameters for newly designed AM materials in an accelerated fashion. Moreover, the majority of existing process optimization approaches involve either time- and cost-intensive experimental campaigns or require the use of proprietary computational materials codes. Through the use of a readily accessible analytical thermal model coupled with statistical calibration and uncertainty quantification techniques, the proposed framework achieves both efficiency and accessibility to the user. Furthermore, this study demonstrates that following this framework results in printed parts with low degrees of variability in their mechanical properties.

Compositionally graded alloys (CGAs) are a specific class of multi-material functionally graded materials (FGMs) that use spatial variations in alloy composition to meet competing performance requirements in at different locations regions of a single part. Directed energy deposition (DED) metal additive technology has enabled the manufacturing of CGAs, but design these alloys remains a challenge. One important challenge is to avoid alloy compositions that result in the formation of deleterious phases during manufacturing. While designers can use CALculation of PHAse Diagram (CALPHAD) models predict the presence of deleterious phases, these calculations tend to be too costly to incorporate directly in a computational design framework. In this work, we apply deep ensembles, or ensembles of deep artificial neural networks (ANNs), to learn a surrogate model of deleterious phase boundaries based on CALPHAD simulations. The learned model is used as a constraint by a path planning algorithm to identify gradient pathway through metal composition space that can be successfully manufactured. We demonstrate the deep ensemble approach in the Fe-Ni-Cr-Ti quaternary system and benchmark it against individual ANNs and a K-nearest neighbors (KNN) approach reported previously. Additionally, we investigate the use of the predicted class probability threshold as a means for understanding surrogate model uncertainty and reasoning about the design space. Lastly, we illustrate how varying the thresholds on constraint probability results in a trade off between manufacturing risk and identifying solutions through narrow passageways.

Accelerating the development of novel materials that have desirable properties is a critical challenge as it can facilitate advances in diverse fields across science, engineering, and medicine with significant contributions to economic growth. For example, the US Materials Genome Initiative calls for cutting the time for bringing new materials from discovery to deployment by half at a fraction of the cost, by integrating experiments, computer simulations, and data analytics. However, the current prevailing practice in materials discovery relies on trial-and-error experimental campaigns and/or high-throughput screening approaches, which cannot efficiently explore the huge design space to develop materials with the targeted properties. Furthermore, measurements of material composition, structure, and properties often contain considerable errors due to technical limitations in materials synthesis and characterization, making this exploration even more challenging. This project aims to develop a software platform for robust autonomous materials discovery that can shift the current trial-and-error practice to an informatics-driven one that can potentially expedite the discovery of novel materials at substantially reduced cost and time. Throughout the project, the PI and Co-PIs will mentor students and equip them with the skills necessary to tackle interdisciplinary problems that involve materials science, computing, optimization, and artificial intelligence. Research findings in the project will be incorporated into the courses taught by the PI and Co-PIs, thereby enriching the learning experience of students. The objective of this project is to develop an effective in-silico experimental design platform to accelerate the discovery of novel materials. The platform will be built on optimal Bayesian learning and experimental design methodologies that can translate scientific principles in materials, physics, and chemistry into predictive models, in a way that takes model and data uncertainty into account. The optimal Bayesian experimental design framework will enable the collection of smart data that can help exploring the material design space efficiently, without relying on slow and costly trial-and-error and/or high-throughput screening approaches. The developed methodologies will be integrated into MSGalaxy, a modular scientific workflow management system, resulting in an accessible, reproducible, and transparent computational platform for accelerated materials discovery that allows easy and flexible customization as well as synergistic contributions from researchers across different disciplines. This project is supported by the Office of Advanced Cyberinfrastructure in the Directorate for Computer & Information Science & Engineering and the Division of Materials Research in the Directorate of Mathematical and Physical Sciences. &nbsp

Automatic Feature Engineering (AFE) aims to extract useful knowledge for interpretable predictions given data for the machine learning tasks. Here, we develop AFE to extract dependency relationships that can be interpreted with functional formulas to discover physics meaning or new hypotheses for the problems of interest. We focus on materials science applications, where interpretable predictive modeling may provide principled understanding of materials systems and guide new materials discovery. It is often computationally prohibitive to exhaust all the potential relationships to construct and search the whole feature space to identify interpretable and predictive features. We develop and evaluate new AFE strategies by exploring a feature generation tree (FGT) with deep Q-network (DQN) for scalable and efficient exploration policies. The developed DQN-based AFE strategies are benchmarked with the existing AFE methods on several materials science datasets.

The inelastic deformation response of NiTi single crystals involves reversible phase transformation and dislocation slip, which is enhanced by the deformation incompatibility among the phases. The phase transformation–plasticity coupling results in decrease in performance, including reduced work output and early fatigue failure. The characterization of the inelastic properties in this material class is crucial for material assessment/ranking and robust performance predictions. Given that direct mesoscale measurements of (coupled) deformation mechanisms are in many cases impractical, top-down characterization of single-crystal properties from limited macroscopic experiments is mostly employed. Here, Bayesian inference and a micromechanics-based continuum single-crystal model are adopted for determining (i) material property values within confidence intervals that allow for a propagation of the quantified uncertainty onto performance predictions, which can be used toward a more efficient design methodology; (ii) ranking of the relative influence of the various material parameters on the deformation response that can further translate to the respective influence of the various deformation mechanisms conditional on the adopted material model; and (iii) a quantitative evaluation of the importance of the deformation incompatibility among the phases in the overall deformation response.

In the present study, Ni95Nb5 (wt. %) alloy samples are additively manufactured using selective laser melting (SLM) as a surrogate for Ni-based superalloys. Near porosity-free samples are fabricated utilizing a simple analytical model to predict melt pool dimensions, guide process parameter selection, and determine the defect-free printability map in the process parameter space. Ni95Nb5 mechanical testing specimens displayed consistent yield strengths (∼600 MPa) and ultimate tensile strengths (∼750 MPa) across a range of process parameters. These results show that with the proper selection of process parameters, SLM can produce parts with consistent mechanical properties in a wide process parameter space in simple alloying systems.

The resistance to oxidizing environments exhibited by some Mn+1AXn (MAX) phases stems from the formation of stable and protective oxide layers at high operating temperatures. The MAX phases are hexagonally arranged layered nitrides or carbides with general formula Mn+1AXn, n = 1, 2, 3, where M is early transition elements, A is A block elements, and X is C/N. Previous attempts to model and assess oxide phase stability in these systems has been limited in scope due to higher computational costs. To address the issue, we developed a machine-learning driven high-throughput framework for the fast assessment of phase stability and oxygen reactivity of 211 chemistry MAX phase M2AX. The proposed scheme combines a sure independence screening sparsifying operator-based machine-learning model in combination with grand-canonical linear programming to assess temperature-dependent Gibbs free energies, reaction products, and elemental chemical activity during the oxidation of MAX phases. The thermodynamic stability, and chemical activity of constituent elements of Ti2AlC with respect to oxygen were fully assessed to understand the high-temperature oxidation behavior. The predictions are in good agreement with oxidation experiments performed on Ti2AlC. We were also able to explain the metastability of Ti2SiC, which could not be synthesized experimentally due to higher stability of competing phases. For generality of the proposed approach, we discuss the oxidation mechanism of Cr2AlC. The insights of oxidation behavior will enable more efficient design and accelerated discovery of MAX phases with maintained performance in oxidizing environments at high temperatures.

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.

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.

Integrated computational materials engineering (ICME) calls for the integration of simulation tools and experiments to accelerate the development of materials. ICME approaches tend to be computationally costly, and recently, Bayesian optimization (BO) has been proposed as a way to make ICME more resource efficient. Conventional BO, however, is sequential (i.e., one-at-a-time) in nature, which makes it very time-consuming when the evaluation of a materials design choice is costly. While conventional high-throughput approaches enable the synthesis and characterization (or simulation) of materials in a parallel manner, they tend to be “open loop” and are unable to provide recommendations of what to try next once the parallel experiment/simulation has been carried out and analyzed. Here, we address this problem by introducing a batch BO framework that enables the exploration of the material’s design space in a parallel fashion. We augment this approach by incorporating information fusion frameworks capable of integrating multiple information sources. Demonstrating the proposed approach in the computational design of dual-phase steel, we show that batch BO can result in a significant reduction in the time and resources needed to carry out the design task. The proposed approach has wider applicability, well beyond the ICME example used to demonstrate it.

We studied the evolution of the Cu6Sn5 and Cu3Sn intermetallic compounds under large thermal gradients for assisted directional solidification in Cu/Sn/Cu micro solder units for fast bonding in 3-D IC packaging. Thermomigration-enhanced growth and dissolution of the phasesare observed and tracked using a CALPHAD-reinforced multiphase-field model that accounts for the thermodynamics and kinetics of the Cu(Sn) reacting system. The results show that the process of bonding follows a reaction-controlled growth regime ( $n{\sim }1$ ) during thermal gradient bonding, proceeding faster than isothermal conditions. In addition, the asymmetric phase evolution, i.e., simultaneous growth in hot side and dissolution in the cold side of the interconnect, is modeled. The developed model can be used for the proactive control of the complex heat treatment processes for the design of multicomponent low-volume solder interconnects in electronic packaging.

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 a...

Machine learning was utilized to efficiently boost the development of soft magnetic materials. The design process includes building a database composed of published experimental results, applying machine learning methods on the database, identifying the trends of magnetic properties in soft magnetic materials, and accelerating the design of next-generation soft magnetic nanocrystalline materials through the use of numerical optimization. Machine learning regression models were trained to predict magnetic saturation ($B_S$), coercivity ($H_C$) and magnetostriction ($\lambda$), with a stochastic optimization framework being used to further optimize the corresponding magnetic properties. To verify the feasibility of the machine learning model, several optimized soft magnetic materials -- specified in terms of compositions and thermomechanical treatments -- have been predicted and then prepared and tested, showing good agreement between predictions and experiments, proving the reliability of the designed model. Two rounds of optimization-testing iterations were conducted to search for better properties.

Laser powder-bed fusion solidification of Ni–Nb alloys often results in cellular morphology in which the solute microsegregation was determined using experiments and simulations, and the data obtai...

Uncovering links between processing conditions, microstructure, and properties is a central tenet of materials analysis. It is well known that microstructure determines properties, but expressing these structural features in a universal quantitative fashion has proved to be extremely difficult. Recent efforts have focused on training supervised learning algorithms to place microstructure images into predefined classes, but this approach assumes a level of a priori knowledge that may not always be available. In this paper, we expand this idea to the semi-supervised context in which class labels are known with confidence for only a fraction of the microstructures that represent the material system. It is shown that classifiers which perform well on both the high-confidence labeled data and the unlabeled, ambiguous data can be constructed by relying on the labeling consensus of a collection of semi-supervised learning methods. We also demonstrate the use of novel error estimation approaches for unlabeled data to establish robust confidence bounds on the classification performance over the entire microstructure space.

Recognizing materials development was advancing slower than technological needs, the 2011 the Materials Genome Initiative (MGI) advocated interdisciplinary approaches employing an informatics framework in materials discovery and development. In response, an interdisciplinary graduate program, funded by the National Science Foundation, was designed at the intersection of materials science, materials informatics, and engineering design, aiming to equip the next generation of scientists and engineers with Material Data Science. Based on the 4- year implementation experience, this report demonstrates how intellectual communities bridge students interdisciplinary learning processes and support a transition from disciplinary grounding to interdisciplinary learning and research. We hope this training model can benefit other interdisciplinary graduate programs, and produce a more productive and interdisciplinary materials workforce.

Significant advances in theory, simulation tools, advanced computing infrastructure, and experimental frameworks have enabled the field of materials science to become increasingly reliant on computer simulations. Theory-grounded computational models provide a better understanding of observed materials phenomena. At the same time, computational tools constitute an important ingredient of any framework that seeks to accelerate the materials development cycle. While simulations keep increasing in sophistication, formal frameworks for the quantification, propagation, and management of their uncertainties are required. Uncertainty analysis is fundamental to any effort to validate and verify simulations, which is often overlooked. Likewise, no simulation-driven materials design effort can be done with any level of robustness without properly accounting for the uncertainty in the predictions derived from the computational models. Here, we review some of the most recent works that have focused on the analysis, quantification, propagation, and management of uncertainty in computational materials science and ICME-based simulation-assisted materials design. Modern concepts of efficient uncertainty quantification and propagation, multi-scale/multi-level uncertainty analysis, model selection as well as model fusion are also discussed. While the topic remains relatively unexplored, there have been significant advances that herald an increased sophistication in the approaches followed for model validation and verification and model-based decision support.

The utilization of metallic anodes holds promise for unlocking high gravimetric and volumetric energy densities and is pivotal to the adoption of ‘beyond Li’ battery chemistries. Much of the promise of magnesium batteries stems from claims regarding their lower predilection for dendrite growth. Whilst considerable effort has been invested in the design of novel electrolytes and cathodes, detailed studies of Mg plating are scarce. Using galvanostatic electrodeposition of metallic Mg from Grignard reagents in symmetric Mg–Mg cells, we establish a phase map characterized by disparate morphologies spanning the range from fractal aggregates of 2D nanoplatelets to highly anisotropic dendrites with singular growth fronts and nanowires entangled in the form of mats. The effects of electrolyte concentration, applied current density, and coordinating ligands have been explored. The study demonstrates a complex range of electrodeposited morphologies including canonical dendrites with shear moduli conducive to penetration through typical polymeric separators. We further demonstrate a strategy for mitigating Mg dendrite formation based on the addition of molecular Lewis bases that promote nanowire growth through selective surface coordination.

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.

We employ a descriptor based machine-learning approach to assess the effect of chemical alloying on formation-enthalpy of rare-earth intermetallics. Application of machine-learning approaches in rare-earth intermetallic design have been sparse due to limited availability of reliable datasets. In this work, we developed an `in-house' rare-earth database with more than 600$+$ compounds, each entry was populated with formation enthalpy and related atomic features using high-throughput density-functional theory (DFT). The SISSO (sure independence screening and sparsifying operator) based machine-learning method with meaningful atomic features was used for training and testing the formation enthalpies of rare earth compounds. The complex lattice function coupled with the machine-learning model was used to explore the effect of transition metal alloying on the energy stability of Ce based cubic Laves phases (MgCu$_{2}$ type). The SISSO predictions show good agreement with high-fidelity DFT calculations and X$-$ray powder diffraction measurements. Our study provides quantitative guidance for compositional considerations within a machine-learning model and discovering new metastable materials. The electronic-structure of Ce$-$Fe$-$Cu based compound was also analyzed in$-$depth to understand the electronic origin of phase stability. The interpretable analytical models in combination with density$-$functional theory and experiments provide a fast and reliable design guide for discovering technologically useful materials., 19 pages, 9 figures

Materials design calls for an optimal exploration and exploitation of the process-structure-property (PSP) relationships to produce materials with targeted properties. Recently, we developed and deployed a closed-loop multi-information source fusion (multi-fidelity) Bayesian Optimization (BO) framework to optimize the mechanical performance of a dual-phase material by adjusting the material composition and processing parameters. While promising, BO frameworks tend to underperform as the dimensionality of the problem increases. Herein, we employ an adaptive active subspace method to efficiently handle the large dimensionality of the design space of a typical PSP-based material design problem within our multi-fidelity BO framework. Our adaptive active subspace method significantly accelerates the design process by prioritizing searches in the important regions of the high-dimensional design space. A detailed discussion of the various components and demonstration of three approaches to implementing the adaptive active subspace method within the multi-fidelity BO framework is presented.

Modeling and simulation for additive manufacturing (AM) are critical enablers for understanding process physics, conducting process planning and optimization, and streamlining qualification and certification. It is often the case that a suite of hierarchically linked (or coupled) simulation models is needed to achieve the above tasks, as the entirety of the complex physical phenomena relevant to the understanding of process-structure-property-performance relationships in the context of AM precludes the use of a single simulation framework. In this study using a Bayesian network approach, we address the important problem of conducting uncertainty quantification (UQ) analysis for multiple hierarchical models to establish process-microstructure relationships in laser powder bed fusion (LPBF) AM. More significantly, we present the framework to calibrate and analyze simulation models that have experimentally unmeasurable variables, which are quantities of interest predicted by an upstream model and deemed necessary for the downstream model in the chain. We validate the framework using a case study on predicting the microstructure of a binary nickel-niobium alloy processed using LPBF as a function of processing parameters. Our framework is shown to be able to predict segregation of niobium with up to 94.3% prediction accuracy on test data.

High-entropy alloys and even medium-entropy alloys are an intriguing class of materials in that structure and property relations can be controlled via alloying and chemical disorder over wide ranges in the composition space. Employing density-functional theory combined with the coherent-potential approximation to average over all chemical configurations, we tune free energies between face-centered-cubic and hexagonal-close-packed phases in ${\mathrm{Fe}}_{x}{\mathrm{Mn}}_{80\ensuremath{-}x}{\mathrm{Co}}_{10}{\mathrm{Cr}}_{10}$ systems. Within Fe-Mn-based alloys, we show that the martensitic transformation and chemical short-range order directly correlate with the face-centered-cubic and hexagonal-close-packed energy difference and stacking-fault energies, which are in quantitative agreement with recent observation of two phase region (face-centered cubic and hexagonal closed pack) in a polycrystalline high-entropy alloy sample at $x=40$ at.%. Our predictions are further confirmed by single-crystal measurements on a $x=40$ at.% using transmission-electron microscopy, selective-area diffraction, and electron-backscattered-diffraction mapping. The results herein offer an understanding of transformation-induced or twinning-induced plasticity in this class of high-entropy alloys and a design guide for controlling the physics at the electronic level.

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)$_{x}$Zr$_{y}$(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$\ge$ 85 at.%, show reproducible hysteresis in stress-strain responsible for pseudo-elastic behavior. The (MoW)$_{85}$Zr$_{7.5}$(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 $\lt$100$\gt$ loading, i.e., the pseudoelasticity is isotropic in loading direction. Additionally, a temperature increase from 77 to 1500 K enhances the elastic strain recovery in load-unload cycles, offering possibly control to tune the pseudoelastic behavior., 29 pages, 13 Figures (9 main figures, 4 supplement figures)

Compositionally graded alloys, a subclass of functionally graded materials (FGMs), utilize localized variations in composition with a single metal part to achieve higher performance than traditional single material parts. In previous work [Kirk, T., Galvan, E., Malak, R., and Arroyave, R., 2018, “Computational Design of Gradient Paths in Additively Manufactured Functionally Graded Materials,” J. Mech. Des., 140, p. 111410. 10.1115/1.4040816], the authors presented a computational design methodology that avoids common issues which limit a gradient alloy’s feasibility, such as deleterious phases, and optimizes for performance objectives. However, the previous methodology only samples the interior of a composition space, meaning designed gradients must include all elements in the space throughout the gradient. Because even small amounts of additional alloying elements can introduce new deleterious phases, this characteristic often neglects potentially simpler solutions to otherwise unsolvable problems and, consequently, discourages the addition of new elements to the state space. The present work improves upon the previous methodology by introducing a sampling method that includes subspaces with fewer elements in the design search. The new method samples within an artificially expanded form of the state space and projects samples outside the true region to the nearest true subspace. This method is evaluated first by observing the sample distribution in each subspace of a 3D, 4D, and 5D state space. Next, a parametric study in a synthetic 3D problem compares the performance of the new sampling scheme to the previous methodology. Lastly, the updated methodology is applied to design a gradient from stainless steel to equiatomic NiTi that has practical uses such as embedded shape memory actuation and for which the previous methodology fails to find a feasible path.

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.

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.

There is increasing awareness of the imperative to accelerate materials discovery, design, development, and deployment. Materials design is essentially a goal-oriented activity that views the material as a complex system of interacting subsystems with models and experiments at multiple scales of materials structure hierarchy. The goal of materials design is effectively to invert quantitative relationships between process path, structure, and materials properties or responses to identify feasible materials. We first briefly discuss challenges in framing process-structure-property relationships for materials and the critical role of quantifying uncertainty and tracking its propagation through analysis and design. A case study exploiting inductive design of ultrahigh-performance concrete is briefly presented. We focus on important recent directions and key scientific challenges regarding the highly collaborative intersections of materials design with systems engineering, uncertainty quantification and management, optimization, and materials data science and informatics, which are essential to fueling continued progress in systems-based materials design.

The design of a chemical synthesis often relies on a combination of chemical intuition and Edisonian trial-and-error methods. Such methods are not just inefficient but inherently limited in their a...