A ductility metric for refractory-based multi-principal-element alloys

We propose a quantum-mechanical dimensionless metric, the local$-$lattice distortion (LLD), as a reliable predictor of ductility in refractory multi-principal-element alloys (RMPEAs). The LLD metric is based on electronegativity differences in localized chemical environments and combines atomic$-$scale displacements due to local lattice distortions with a weighted average of valence$-$electron count. To evaluate the effectiveness of this metric, we examined body$-$centered cubic (bcc) refractory alloys that exhibit ductile$-$to$-$brittle behavior. Our findings demonstrate that local$-$charge behavior can be tuned via composition to enhance ductility in RMPEAs. With finite$-$sized cell effects eliminated, the LLD metric accurately predicted the ductility of arbitrary alloys based on tensile$-$elongation experiments. To validate further, we qualitatively evaluated the ductility of two refractory RMPEAs, i.e., NbTaMoW and Mo$_{72}$W$_{13}Ta$_{10}Ti$_{2.5}Zr$_{2.5}, through the observation of crack formation under indentation, again showing excellent agreement with LLD predictions. A comparative study of three refractory alloys provides further insights into the electronic-structure origin of ductility in refractory RMPEAs. This proposed metric enables rapid and accurate assessment of ductility behavior in the vast RMPEA composition space.Comment: 36 pages, 12 figures, 5 Tabl

An Automated Fully-Computational Framework to Construct Printability Maps for Additively Manufactured Metal Alloys

In additive manufacturing, the optimal processing conditions need to be determined to fabricate porosity-free parts. For this purpose, the design space for an arbitrary alloy needs to be scoped and analyzed to identify the areas of defects for different laser power-scan speed combinations and can be visualized using a printability map. Constructing printability maps is typically a costly process due to the involvement of experiments, which restricts their application in high-throughput product design. To reduce the cost and effort of constructing printability maps, a fully computational framework is introduced in this work. The framework combines CALPHAD models and a reduced-order model to predict material properties. THen, an analytical thermal model, known as the Eagar-Tsai model, utilizes some of these materials' properties to calculate the melt pool geometry during the AM processes. In the end, printability maps are constructed using material properties, melt pool dimensions, and commonly used criteria for lack of fusion, balling, and keyholing defects. To validate the framework and its general application to laser powder-bed fusion alloys, five common additive manufacturing alloys are analyzed. Furthermore, NiTi-based alloys at three different compositions are evaluated to show the further extension of the framework to alloy systems at different compositions. The defect regions in these printability maps are validated with corresponding experimental observations to compare and benchmark the defect criteria and find the optimal criterion set with the maximum accuracy for each unique material composition. Furthermore, printability maps for NiTi that are obtained from our framework are used in conjunction with process maps resulting from a multi-model framework to guide the fabrication of defect-free additive manufactured parts with tailorable properties and performance.Comment: 18 Figures, 35 page

High-throughput Alloy and Process Design for Metal Additive Manufacturing

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 figure

Entropy-driven melting point depression in fcc HEAs

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 phase, 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 CoCrFeMnNiV-Al HEA space using a state of the art CALPHAD database. By using modern visualization techniques and statistical analysis we examine the trade-off between chemical complexity and stability against the liquid state and identify a potentially difficult to overcome barrier for development of high temperature alloys, at least within the conventional fcc HEA space. Limited experimental data seem to be consistent with this analysis

Multi-objective materials bayesian optimization with active learning of design constraints: Design of ductile refractory multi-principal-element alloys

Bayesian Optimization (BO) has emerged as a powerful framework to efficiently explore and exploit materials design spaces. To date, most BO approaches to materials design have focused on the materials discovery problem as if it were a single expensive-to-query ‘black box’ in which the target is to optimize a single objective (i.e., material property or performance metric). Also, such approaches tend to be constraint agnostic. Here, we present a novel multi-information BO framework capable of actively learning materials design as a multiple objectives and constraints problem. We demonstrate this framework by optimally exploring a Refractory Multi-Principal-Element Alloy (MPEA) space, here specifically, the system Mo-Nb-Ti-V-W. The MPEAs are explored to optimize two density-functional theory (DFT) derived ductility indicators (Pugh’s Ratio and Cauchy pressure) while learning design constraints relevant to the manufacturing of high-temperature gas-turbine components. Alloys in the BO Pareto-front are analyzed using DFT to gain an insight into fundamental atomic and electronic underpinning for their superior performance, as evaluated within this framework.This is a manuscript of an article published as Khatamsaz, Danial, Brent Vela, Prashant Singh, Duane D. Johnson, Douglas Allaire, and Raymundo Arróyave. "Multi-objective materials bayesian optimization with active learning of design constraints: Design of ductile refractory multi-principal-element alloys." Acta Materialia 236 (2022): 118133. DOI: 10.1016/j.actamat.2022.118133. Copyright 2022 Acta Materialia Inc. Posted with permission. DOE Contract Number(s): AR0001427; AC02-07CH11358; DGE-1545403; CDSE-2001333; CISE-1835690; DMREF-2119103

Bayesian optimization with active learning of design constraints using an entropy-based approach

The design of alloys for use in gas turbine engine blades is a complex task that involves balancing multiple objectives and constraints. Candidate alloys must be ductile at room temperature and retain their yield strength at high temperatures, as well as possess low density, high thermal conductivity, narrow solidification range, high solidus temperature, and a small linear thermal expansion coefficient. Traditional Integrated Computational Materials Engineering (ICME) methods are not sufficient for exploring combinatorially-vast alloy design spaces, optimizing for multiple objectives, nor ensuring that multiple constraints are met. In this work, we propose an approach for solving a constrained multi-objective materials design problem over a large composition space, specifically focusing on the Mo-Nb-Ti-V-W system as a representative Multi-Principal Element Alloy (MPEA) for potential use in next-generation gas turbine blades. Our approach is able to learn and adapt to unknown constraints in the design space, making decisions about the best course of action at each stage of the process. As a result, we identify 21 Pareto-optimal alloys that satisfy all constraints. Our proposed framework is significantly more efficient and faster than a brute force approach.This article is published as Khatamsaz, Danial, Brent Vela, Prashant Singh, Duane D. Johnson, Douglas Allaire, and Raymundo Arróyave. "Bayesian optimization with active learning of design constraints using an entropy-based approach." npj Computational Materials 9, no. 1 (2023): 1-14. DOI: 10.1038/s41524-023-01006-7. Copyright 2023 The Author(s). Attribution 4.0 International (CC BY 4.0). Posted with permission. DOE Contract Number(s): AC02–07CH11358; AC02-07CH11358; AR0001427; W911NF2220106; DGE-1545403; CDSE-2001333; NSF-CISE-1835690; NSF-DMREF-211910

Bayesian optimization with active learning of design constraints using an entropy-based approach

Abstract The design of alloys for use in gas turbine engine blades is a complex task that involves balancing multiple objectives and constraints. Candidate alloys must be ductile at room temperature and retain their yield strength at high temperatures, as well as possess low density, high thermal conductivity, narrow solidification range, high solidus temperature, and a small linear thermal expansion coefficient. Traditional Integrated Computational Materials Engineering (ICME) methods are not sufficient for exploring combinatorially-vast alloy design spaces, optimizing for multiple objectives, nor ensuring that multiple constraints are met. In this work, we propose an approach for solving a constrained multi-objective materials design problem over a large composition space, specifically focusing on the Mo-Nb-Ti-V-W system as a representative Multi-Principal Element Alloy (MPEA) for potential use in next-generation gas turbine blades. Our approach is able to learn and adapt to unknown constraints in the design space, making decisions about the best course of action at each stage of the process. As a result, we identify 21 Pareto-optimal alloys that satisfy all constraints. Our proposed framework is significantly more efficient and faster than a brute force approach

A ductility metric for refractory-based multi-principal-element alloys

We propose a quantum-mechanical dimensionless metric, the local−lattice distortion (LLD), as a reliable predictor of ductility in refractory multi-principal-element alloys (RMPEAs). The LLD metric is based on electronegativity differences in localized chemical environments and combines atomic−scale displacements due to local lattice distortions with a weighted average of valence−electron count. To evaluate the effectiveness of this metric, we examined body−centered cubic (bcc) refractory alloys that exhibit ductile−to−brittle behavior. Our findings demonstrate that local−charge behavior can be tuned via composition to enhance ductility in RMPEAs. With finite−sized cell effects eliminated, the LLD metric accurately predicted the ductility of arbitrary alloys based on tensile−elongation experiments. To validate further, we qualitatively evaluated the ductility of two refractory RMPEAs, i.e., NbTaMoW and Mo72W13Ta_{10}Ti2.5Zr_{2.5}, through the observation of crack formation under indentation, again showing excellent agreement with LLD predictions. A comparative study of three refractory alloys provides further insights into the electronic-structure origin of ductility in refractory RMPEAs. This proposed metric enables rapid and accurate assessment of ductility behavior in the vast RMPEA composition space.This is a preprint from Singh, Prashant, Brent Vela, Gaoyuan Ouyang, Nicolas Argibay, Jun Cui, Raymundo Arroyave, and Duane D. Johnson. "A ductility metric for refractory-based multi-principal-element alloys." Acta Materialia 257 (2023): 119104. doi: https://doi.org/10.48550/arXiv.2211.15797. Copyright 2023, The Authors. CC-BY

High-throughput exploration of the WMoVTaNbAl refractory multi-principal-element alloys under multiple-property constraints

Development of next-generation gas turbines requires the design and fabrication of novel high-temperature structural materials capable of operating beyond 1300°C. We propose a high-throughput alloy design framework under multiple-property constraints to discover new refractory multi-principal element alloys (MPEAs) for high-temperature applications. The framework treats the development of MPEAs as a composition-agnostic constraint satisfaction problem, i.e., no prescriptions are made concerning the design space before performing investigatory calculations. We target alloys in the WMoVTaNbAl chemistry space that are predicted to meet constraints on the following properties simultaneously: single-phase stability, density, solidus temperature, yield strength at 1300°C, and ductile-to-brittle-transition temperature. These properties are relevant to both applications in gas turbines and manufacturability. A set of 214 MoNbV-rich alloys meet these relevant constraints. These feasible alloys are investigated with density functional theory (DFT) to provide a fundamental electronic basis for their superior properties. Three compositionally representative alloys from the feasible design space (Mo45Nb35Ta5V15, Mo25Nb50V20W5, and Mo30Nb35Ta5V25W5) are selected with a k-medoids-based design scheme for detailed DFT analysis and experimental characterization. The DFT analysis predicted a single-phase BCC at high temperatures with a high yield strength for all three MPEAs, in agreement with CALPHAD (CALculation of PHAse Diagrams) and experiments, respectively. These three alloys are benchmarked against a public database of 1546 MPEAs. Concerning the aforementioned constraints, the Mo30Nb35Ta5V25W5 alloy outperforms these 1546 MPEAs. The present work demonstrates the ability of the proposed design methodology to identify candidate alloys for a given application under multiple property constraints in a combinatorically vast design space.This is a manuscript of an article published as Vela, Brent, Cafer Acemi, Prashant Singh, Tanner Kirk, William Trehern, Eli Norris, Duane D. Johnson, Ibrahim Karaman, and Raymundo Arróyave. "High-throughput exploration of the WMoVTaNbAl refractory multi-principal-element alloys under multiple-property constraints." Acta Materialia 248 (2023): 118784. DOI: 10.1016/j.actamat.2023.118784. Copyright 2023 Acta Materialia Inc. Posted with permission. DOE Contract Number(s): AC02-07CH11358; AR0001427; 1545403; 1746932; 1835690; 2119103; 80NSSC21K022