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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Transferable predictions of energetic and structural properties for refractory solid solution alloys across chemical compositions

We present a data-efficient approach to train graph neural networks (GNNs) on density functional theory (DFT) data for accurate and transferable predictions of energetic and structural properties of refractory solid solution alloys in the niobium-tantalum-vanadium (Nb-Ta-V) chemical space. We start by training the GNN model only on DFT data that describes refractory binary alloys niobium-tantalum (Nb-Ta), niobium-vanadium (Nb-V), and tantalum-vanadium (Ta-V) to predict formation enthalpy and root mean squared displacement. Once trained, the GNN predictions are tested on DFT data describing refractory ternary alloys Nb-Ta-V. While, unsurprisingly, direct transferability from binary to ternary is not sufficiently accurate, augmenting the training with only 1% of the available ternary data (uniformly distributed across the entire range of chemical compositions) improves significantly the quality of the GNN predictions. For comparison, we assess the transferability in the opposite direction by training GNN models on ternary Nb-Ta-V data and making predictions on binaries Nb-Ta, Nb-V, and Ta-V, which exhibits notably higher predictive errors. The proposed methodology, which favors transferability from lower-component to higher-component alloys, offers an efficient path towards avoiding the curse of dimensionality incurred when collecting DFT data for discovery and design of multi-component disordered alloys.

Density functional theory calculations↗
DOE OSTI2022

Microstructure, compression properties, and oxidation behavior of Hf-25Ta-5Me alloys (Me is Mo, Nb, W, 0.5Mo + 0.5 W, Cr, or Zr)

Hf–Ta based alloys have recently been investigated as potential candidates for high-temperature structural applications. While most attentions have been given to the properties above ~1200 °C where the alloys are mainly single-phase BCC structures and have excellent oxidation performance due to formation of super-oxides, structural properties at lower temperatures are equally important for applications in which an alloy may experience a range of temperatures. In the present work, microstructure, phase composition, mechanical properties and oxidation behavior of six Hf-25Ta-5Me alloys (Me is Mo (HTM alloy), W (HTW), 0.5Mo + 0.5 W (HTMW), Cr (HTC) or Zr (HTZ), the compositions are in at.%) are reported at temperatures below the eutectoid transformation. The alloys were prepared by arc melting followed by hot isostatic pressing for 3 h at 1400 °C and 207 MPa. All the alloys display coarse grains of partially or fully transformed (by a eutectoid reaction) high-temperature BCC phase. The eutectoid regions consist of fine lamellae of Hf-rich HCP and Ta-rich BCC phases. The ternary alloys containing W or Cr also contained small amounts of a cubic Laves phase. At 25 °C, the HTW alloy was the strongest (yield stress σ y = 1966 MPa) but brittle and HTZ was the weakest (σ y = 1120 MPa) but ductile among the studied alloys. Other alloys showed intermediate behaviors. In general, the room temperature ductility of the alloys increased with decreasing σy. All alloys maintained high strength up to 800 °C, but displayed a noticeable strength decrease at 1000 °C. At 1000 °C, HTMW was the strongest alloy (σ y = 468 MPa) and HTC was the weakest alloy (σ y = 368 MPa). All alloys had excellent deformability at 1000 °C. Oxidation behavior of the alloys was studied at 800 °C and 1000 °C and compared with that of Hf–27Ta binary alloy. Although the ternary additions improved oxidation resistance, overall oxidation performance of the studied alloys at 800 °C and 1000 °C was poor.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Benchmark Specifications for TREAT Tests M5, M6, and M7

Detailed information describing three TREAT tests performed on metallic fuels has been collected and organized for use as a benchmark in evaluating the performance of metallic fuel models and codes. The tests, designated M5, M6, and M7, subjected EBR-II-irradiated fuel pins to a single type of overpower transient (at prototypical conditions with full coolant flow and an exponential power rise on an 8 second period) in flowing sodium loops. Six fuel pins were tested; five were ternary (U-19Pu-10Zr) alloy fuel clad in D9 with burnup ranging from 0.8 to 9.8 at. %, and one was binary alloy fuel (U-10Zr) clad in HT9 with 2.9 at. % burnup. The information gathered from the test records is expected to be useful for pre-transient characterization of the irradiated fuel pins as well as the transient analysis of the metallic fuel when subjected to severe accident conditions. This report presents benchmark specifications for the M5, M6, and M7 TREAT tests, identifies where primary sources of benchmark-related information can be found, and includes background information to help a user of the data understand their applicability and limitations.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

The Efficacy of Replacing Metallic Cerium in Aluminum–Cerium Alloys with LREE Mischmetal

The ongoing development of aluminum–cerium alloys has produced materials exhibiting elevated temperature mechanical property retention, long term microstructural stability, and flexible processability compared to traditional aluminum alloys, accommodating the growing demand for high temperature aluminum alloys not requiring the use of high-cost elements like scandium. To date, reported Al–Ce alloy compositions contain large amounts of elemental cerium. Mischmetal (MM), a mixture of lanthanum, cerium, and other light rare earth elements (LREE) is less expensive and more available than pure cerium. The chemical similarity of the LREEs means there is possibility to use MM as the primary alloy addition, lowering alloy cost. This talk will report the effect of using MM instead of cerium in a 12 wt% binary alloy on mechanical properties, phase constituency, thermal stability, and load sharing. Results will show that MM can be substituted completely for cerium with a mostly positive impact on alloy performance.

Sims, Zachary↗
DOE OSTI2020

Learning grain boundary segregation energy spectra in polycrystals

The segregation of solute atoms at grain boundaries (GBs) can profoundly impact the structural properties of metallic alloys, and induce effects that range from strengthening to embrittlement. And, though known to be anisotropic, there is a limited understanding of the variation of solute segregation tendencies across the full, multidimensional GB space, which is critically important in polycrystals where much of that space is represented. Here we develop a machine learning framework that can accurately predict the segregation tendency—quantified by the segregation enthalpy spectrum—of solute atoms at GB sites in polycrystals, based solely on the undecorated (pre-segregation) local atomic environment of such sites. We proceed to use the learning framework to scan across the alloy space, and build an extensive database of segregation energy spectra for more than 250 metal-based binary alloys. The resulting machine learning models and segregation database are key to unlocking the full potential of GB segregation as an alloy design tool, and enable the design of microstructures that maximize the useful impacts of segregation.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Fast and Accurate Predictions of Total Energy for Solid Solution Alloys with Graph Convolutional Neural Networks

We use graph convolutional neural networks (GCNNs) to produce fast and accurate predictions of the total energy of solid solution binary alloys. GCNNs allow us to abstract the lattice structure of a solid material as a graph, whereby atoms are modeled as nodes and metallic bonds as edges. This representation naturally incorporates information about the structure of the material, thereby eliminating the need for computationally expensive data pre-processing which would be required with standard neural network (NN) approaches. We train GCNNs on ab-initio density functional theory (DFT) for copper-gold (CuAu) and iron-platinum (FePt) data that has been generated by running the LSMS-3 code, which implements a locally self-consistent multiple scattering method, on OLCF supercomputers Titan and Summit. GCNN outperforms the ab-initio DFT simulation by orders of magnitude in terms of computational time to produce the estimate of the total energy for a given atomic configuration of the lattice structure. We compare the predictive performance of GCNN models against a standard NN such as dense feedforward multi-layer perceptron (MLP) by using the root-mean-squared errors to quantify the predictive quality of the deep learning (DL) models. We find that the attainable accuracy of GCNNs is at least an order of magnitude better than that of the MLP.

Lupo Pasini, Massimiliano↗
DOE OSTI2021

Sluggish, chemical bias and percolation phenomena in atomic transport by vacancy and interstitial diffusion in Ni-Fe alloys

In this paper, we report the results of extended atomistic modeling of intrinsic mobility of point defects and associated atomic transport in Ni-Fe model binary alloys. We consider the effects of composition and temperature and present evidence of the sluggish and chemically biased diffusion, and percolation effects occurring in atomic transport via the vacancy and interstitial migration mechanisms. The results are analyzed and discussed in the light of previous studies and some experimental observations. It is demonstrated that the sluggish diffusion, the chemically biased diffusion, and the percolation are interlinked phenomena that are defined by the chemical complexity of particular alloys. Methods for predicting these phenomena in multicomponent alloys are discussed.We report a fundamental understanding of sluggish diffusion, chemically-biased diffusion, as well as percolation phenomena, in Ni-Fe random alloys for vacancy and interstitial atom migration mechanisms.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Development of HPC based phase field simulations tool for modification of alloy morphology to enhance material properties during additive manufacturing (AM) process

Currently, additive manufacturing (AM) technology is extensively used to manufacture aerospace components because of the unique advantages of the technique in producing custom designs, complex geometries in a near-net fashion that significantly reduces the manufacturing cost, and improved energy efficiency. However, the thermodynamic properties of existing aerospace alloys and the thermal conditions that prevail in most of the powder-bed based additive manufacturing processes lead to undesirable columnar / columnar-dendritic microstructures that promote solidification cracking during the process, and the development of anisotropy in the mechanical properties of the as-built components. Time consuming, energy-intensive post AM treatments are required to recover the mechanical properties of the wrought components for which the existing alloy compositions were designed for. However, novel alloy compositions, that exploit the AM thermal conditions to produce fine, equiaxed grain structures in the as-built condition, with mechanical properties equal to or exceeding those of the wrought work-horse alloy, Ti-6Al-4V has recently been demonstrated in Ti-Cu alloys. Significant energy savings up to 66% can be obtained for a typical aerospace component through digitally designing the AM process, process parameters, and alloy composition. The current project focused on developing a fundamental understanding of the evolution of the columnar to equiaxed transition (CET) occurring during solidification under AM thermal conditions in Ti-Cu and Ti-Cu-X alloys using high-fidelity phase field (PF) simulations using the leadership class computing facilities that exist in the national laboratories. The simulations were able to capture the effect of alloy and process conditions on CET, and clearly showed the beneficial effect of a ternary solute addition to Ti-Cu binary alloys on CET. The simulations indicated that bulk nucleation in the liquid ahead of the solidifying epitaxial front was promoted by a large, transient, thermal undercooling promoted by the mismatch between the growth rate of the dendrite tips in the epitaxial front and the imposed solidification rate. AM experiments performed at Raytheon Technologies Research Center using the same thermal conditions used in the PF simulations indicated the formation of equiaxed grains, although the grain size was bigger than the ones reported in the literature. On the other hand, the equiaxed grain size predicted by PF simulations were smaller than the grain size of Ti-Cu alloys reported in the literature. The industry will follow up the existing work in a future program that would involve further optimization of the process for the Ti-Cu-X alloys to demonstrate the application of the process and the alloy to a specific aerospace component.

36 MATERIALS SCIENCE↗
DOE OSTI2023

ORNL_AISD_NiPt_108atoms

This dataset describes the nickel-platinum (NiPt) solid solution binary alloy, where the two constituent elements nickel (Ni) and platinum (Pt) are randomly placed on the face centered cubic (FCC) crystal structure, with the lattice constant of 3.840 angstroms. The dataset comprises data for crystal structures with 108 atoms with 1,900 configurations. The data set was generated for concentrations ranging from 0at% of Pt to 100at% of Pt in the NiPt binary system, with increasing the concentration of Pt in the system every 5at%. For each one of the chemical compositions, 100 random configurations were generated, each with a different random seed. Each of the output files contains the mass, type, atomic coordinates, energy per atom, and forces in x, y, and z directions respectively. For each atomic configuration, the output was collected every 150 steps during the minimization stage and every 1000 steps during the replica exchange stage. Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) [1], which is a molecular dynamics code, was used to generate data for NiPt alloy. The simulation used the interatomic potential for NiPt binary system 'MEAM_LAMMPS_KimSeolJi_2017_PtNi__MO_020840179467_001' [3] from the OpenKIM library (Open Knowledgebase of Interatomic Models) [2]. This potential was developed based on the second nearest-neighbor modified embedded-atom method (2NN MEAM). The simulation process begins with the generation of the random NiPt structure and follows with the short minimization and replica exchange simulation. The minimization procedure adjusts atomic coordinates and performs energy minimization, which typically leads to a local potential energy minimum. The method used for the minimization was the conjugate gradient algorithm. A short replica exchange (parallel tempering) simulation involves four replicas (ensembles) of a system and follows the minimization stage. Multiple snapshots of the configuration were collected during the minimization and replica exchange stages. NiPt alloy is interesting due to its magnetic and charge transfer properties [4]. The data is provided in a compressed zipped folders atoms108.zip. The zipped folder contains the data structured in the following way: - Ni_ground_state.cfg --> atomic configuration for the pure nickel - Pt_ground_state.cfg --> atomic configuration for the pure platinum - Pt#_filtered --> folders containing atomic configurations for #at% concentration of platinum. The folder contains 100 atomic configurations, each saved in a subfolder - Each subfolder named config* is associated with a specific atomic configuration. Each of these subfolders contains files with .cfg format, corresponding to outputs for each atomic configuration The total number of atomic configurations contained in atoms108.zip is 66,132. This dataset is an extension to the dataset ORNL_AISD_NiPt [5] that has been previously released with crystal structures of 256 atoms, 864 atoms, and 2,048 atoms, with the same methodology for data collection. References [1] https://www.lammps.org/ [2] https://openkim.org/ [3] https://openkim.org/id/MEAM_LAMMPS_KimSeolJi_2017_PtNi__MO_020840179467_001 [4] El-Gendy, Ahmed A. and Hampel, Silke and Büchner, Bernd and Klingeler, Rüdiger, Tuneable magnetic properties of carbon-shielded NiPt-nanoalloys, RSC Adv., volume 6, issue 57, pages 52427-52433, 2016, The Royal Society of Chemistry, doi:10.1039/C6RA05910D [5] M. Karabin, M. Lupo Pasini, and M. Eisenbach. ORNL_AISD_NiPt. United States: N. p., 2023. Web. doi:10.13139/OLCF/1958172.

36 MATERIALS SCIENCE↗
DOE OSTI2023

ORNL_AISD_NiPt

This dataset describes the nickel-platinum (NiPt) solid solution binary alloy, where the two constituent elements nickel (Ni) and platinum (Pt) are randomly placed on the face centered cubic (FCC) crystal structure, with the lattice constant of 3.840 angstroms. The dataset comprises data for three different sizes of the crystal structure: 256 atoms, 864 atoms, and 2,048 atoms, each of which contains 1900 configurations. For each size of the crystal structure, the data set was generated for concentrations ranging from 0at% of Pt to 100at% of Pt in the NiPt binary system, with increasing the concentration of Pt in the system every 5at%. For each one of the chemical compositions, 100 random configurations were generated, each with a different random seed. Each of the output files contains the mass, type, atomic coordinates, energy per atom, and forces in x, y, and z directions respectively. For each atomic configuration, the output was collected every 150 steps during the minimization stage and every 1000 steps during the replica exchange stage. Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) [1], which is a molecular dynamics code, was used to generate data for NiPt alloy. The simulation used the interatomic potential for NiPt binary system MEAM_LAMMPS_KimSeolJi_2017_PtNi__MO_020840179467_001 [3] from the OpenKIM library (Open Knowledgebase of Interatomic Models) [2]. This potential was developed based on the second nearest-neighbor modified embedded-atom method (2NN MEAM). The simulation process begins with the generation of the random NiPt structure and follows with the short minimization and replica exchange simulation. The minimization procedure adjusts atomic coordinates and performs energy minimization, which typically leads to a local potential energy minimum. The method used for the minimization was the conjugate gradient algorithm. A short replica exchange (parallel tempering) simulation involves four replicas (ensembles) of a system and follows the minimization stage. Multiple snapshots of the configuration were collected during the minimization and replica exchange stages. NiPt alloy is interesting due to its magnetic and charge transfer properties [4]. The data is provided in three compressed zipped folders: atoms256.zip, atoms864.zip, atoms2048.zip Each zipped folder contains the data that describes crystals of size 256 atoms, 864 atoms, and 2,048 atoms respectively. Each one of the three zipped folders contains the data structured in the following way: -Ni_ground_state.cfg --> atomic configuration for the pure nickel -Pt_ground_state.cfg --> atomic configuration for the pure platinum -Pt#_filtered --> folders containing atomic configurations for #at% concentration of platinum. The folder contains 100 atomic configurations, each saved in a subfolder. Each subfolder named config* is associated with a specific atomic configuration. Each of these subfolders contains files with .cfg format, corresponding to outputs for each atomic configuration The total number of atomic configurations contained in atoms256.zip is 65,046. The total number of atomic configurations contained in atoms864.zip is 63,936. The total number of atomic configurations contained in atoms2048.zip is 61,997. The total number of atomic configurations spanned by the entire dataset is 190,979. References [1] https://www.lammps.org/ [2] https://openkim.org/ [3] https://openkim.org/id/MEAM_LAMMPS_KimSeolJi_2017_PtNi__MO_020840179467_001 [4] El-Gendy, Ahmed A. and Hampel, Silke and Büccchner, Bernd and Klingeler, Rüdiger, Tuneable magnetic properties of carbon-shielded NiPt-nanoalloys, RSC Adv., volume 6, issue 57, pages 52427-52433, 2016, The Royal Society of Chemistry, doi:10.1039/C6RA05910D

36 MATERIALS SCIENCE↗
DOE OSTI2023

A machine learning framework for elastic constants predictions in multi-principal element alloys

On the one hand, multi-principal element alloys (MPEAs) have created a paradigm shift in alloy design due to large compositional space, whereas on the other, they have presented enormous computational challenges for theory-based materials design, especially density functional theory (DFT), which is inherently computationally expensive even for traditional dilute alloys. In this paper, we present a machine learning framework, namely PREDICT (PRedict properties from Existing Database In Complex alloys Territory), that opens a pathway to predict elastic constants in large compositional space with little computational expense. The framework only relies on the DFT database of binary alloys and predicts Voigt–Reuss–Hill Young’s modulus, shear modulus, bulk modulus, elastic constants, and Poisson’s ratio in MPEAs. We show that the key descriptors of elastic constants are the A–B bond length and cohesive energy. The framework can predict elastic constants in hypothetical compositions as long as the constituent elements are present in the database, thereby enabling property exploration in multi-compositional systems. We illustrate predictions in a FCC Ni-Cu-Au-Pd-Pt system.

Linton, Nathan (ORCID:0000000315485613)↗
DOE OSTI2022

Potentiodynamic Polarization of Pure Metals and Alloys in Molten LiF-NaF-KF (FLiNaK) Using the K/K + Dynamic Reference Electrode

The potentiodynamic polarization method using the K/K + dynamic reference electrode was used to understand the corrosion behavior of Ni, Fe, Cr, the Ni-20Cr binary alloy, and 316L stainless steel in molten 46.5LiF-11.5NaF-42KF (FLiNaK, composition in mol %) salt at 700 °C. Exposure of the materials without an applied potential revealed that Ni and Fe were stable in FLiNaK, but Cr was dissolved rapidly. The apparent redox potentials of each material with respect to the K/K + redox couple are compared to gain insight into how the applied potential relates to the dominant corrosion mechanism for each alloy. Furthermore, the polarized pure metals experienced uniform surface recession while corrosion in the alloys resulted in preferential elemental dissolution depending on the applied potential with respect to the K/K + reference reaction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Understanding Twinning and Deformation in High Entropy Alloys

On the one hand, multi-principal element alloys (MPEAs) have created a paradigm shift in alloy design due to large compositional space, whereas on the other, they have presented enormous computational challenges for theory-based materials design, especially density functional theory (DFT), which is inherently computationally expensive even for traditional dilute alloys. In this project, we developed a machine learning framework, namely PREDICT ( PR edict properties from E xisting D atabase I n C omplex alloys T erritory), that opens a pathway to predict elastic constants in large compositional space with little computational expense. The framework only relies on the DFT database of binary alloys and predicts Voigt–Reuss–Hill Young’s modulus, shear modulus, bulk modulus, elastic constants, and Poisson’s ratio in MPEAs. We show that the key descriptors of elastic constants are the A–B bond length and cohesive energy. The framework can predict elastic constants in hypothetical compositions as long as the constituent elements are present in the database, thereby enabling property exploration in multi-compositional systems. We illustrate predictions in a FCC Ni-Cu-Au-Pd-Pt system.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Formation enthalpies of Al–Mn–Pd and the structure of the i-AlMnPd quasicrystal

Abstract This paper reports formation enthalpies of phases in the Al–Mn–Pd ternary alloy system as calculated from first principles using electronic density functional theory. We consider all crystal structures as reported in the assessed phase diagrams of the ternary and its binary alloy subsystems (Al–Mn, Al–Pd, and Mn–Pd), as well as additional reported or hypothetical structures. Icosahedral and decagonal quasicrystalline approximants are among the structures that we predict to be stable, or nearly so. Our results suggest the need for careful experimental reexamination of phase stability in each of the alloy systems, in tandem with further efforts to refine crystallographic and ab-initio structures.

17 WIND ENERGY↗
DOE OSTI2020

High-Throughput Exploration of Lithium-Alloy Protection Layers for High-Performance Lithium-Metal Batteries

To realize high specific capacity Li-metal batteries, a protection layer for the Li-metal anode is needed. We are carrying out combinatorial screening of Li-alloy thin films as the protection layer which can undergo significant lithiation with minimum change in volume and crystal structure. In this paper, we have fabricated lithium-free binary alloy thin film composition spreads of Co 1–x Sn x on Cu layers on Si substrates. The crystallinity of the thin films was tuned by varying the deposition temperature followed by electrochemical lithiation to form Li-alloy ternary thin films. Synchrotron diffraction is used as the main tool to investigate the crystallinity of the films before and after lithiation. Co 3 Sn 2 alloy thin films are found to exhibit significant lithium uptake capacity while maintaining its structural integrity, and are thus a good candidate of the Li-metal protection layer.

25 ENERGY STORAGE↗
DOE OSTI2023

A comparative study of solute trapping in Fe-(33–45 at%) Cu alloys manufactured by laser directed energy deposition

The high cooling rates in laser directed energy deposition (DED-LB) of alloys lead to substantial amounts of solute trapping as solute atoms cannot diffuse away from the solid/liquid interface before it advances. In some concentrated alloys, this results in supersaturated solid phases that form nanoscale hierarchical microstructures when the solute atoms precipitate out during reheating from subsequent laser passes. We choose the Iron-Copper (Fe-Cu) binary alloy as model system as it is chemically homogeneous in the liquid phase and has negligible solid solubility at room temperature. Two alloys with nominal compositions in atomic (at.) %, Fe67Cu33 and Fe55Cu45, were manufactured using DED-LB. Scanning transmission electron microscopy (STEM), energy dispersive spectroscopy (EDS) and wavelength dispersive spectroscopy (WDS) were used to characterize the nanostructures and heterogeneous chemical compositions. A non-equilibrium solute partitioning model was used to compute the supersaturated chemistries of the constituent phases and validated with experimentally measured compositions. The measured phase compositions of the two alloys were very similar, at roughly 12 at.% Cu and 4 at.% Fe in the α(bcc)-Fe and ε(fcc)-Cu phases respectively, despite having different processing parameters and mechanical behavior. This indicates that the total thermal history, that depends on both the processing parameters and laser scan pattern, plays a stronger role on the final microstructure evolution than just the initial quantity of trapped solute. In addition, we find that current non-equilibrium solute partitioning models applied on the continuum scale fall short of predicting accurate quantitative phase compositions in concentrated alloys, although the qualitative trends are captured correctly.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Chemomechanics in alloy phase stability

We describe a first-principles statistical mechanics method to calculate the free energies of crystalline alloys that depend on temperature, composition, and strain. The approach relies on an extension of the alloy cluster expansion to include an explicit dependence on homogeneous strain in addition to site occupation variables that track the degree of chemical ordering. The method is applied to the Si-Ge binary alloy and is used to calculate free energies that describe phase stability under arbitrary epitaxial constraints. We find that while the incoherent phase diagram (in which coexisting phases are not affected by coherency constraints) hosts a miscibility gap, coherent phase equilibrium predicts ordering and negative enthalpies of mixing. Instead of chemical instability, the chemomechanical free energy exhibits instabilities along directions that couple the composition of the alloy with a volumetric strain order parameter. Furthermore, this has fundamental implications for phase field models of spinodal decomposition as it indicates the importance of gradient energy coefficients that couple gradients in composition with gradients in strain.

Materials Science↗
DOE OSTI2025

Analysis of correlations between intrinsic ductility and electronic density of states in refractory alloys

High entropy alloys (HEAs) correspond to a new and emerging class of materials that allows us to explore a large composition space to tune mechanical strength and thermal stability. Therefore, to design better alloys, it is important to scan the high-dimensional space of chemistry, composition and temperature. Here, to facilitate this search, we present a method to screen intrinsically ductile body centered cubic (BCC) refractory alloys from electronic structure calculations by using the density of states (DOS) at the Fermi level, g(μ F ). This correlation between intrinsic ductility and g(μ F ) is tested by analyzing group V (V, Nb, Ta) and VI (Mo, W) refractory metals, binary alloys, such as W-Nb, W-V, Mo-Nb and Mo-V, and refractory alloys for which experimental stress-strain measurements are available. In addition, we perform a high-throughput exploration of the entire composition space of a recently proposed alloy system, CrMoNbV, and identify compositions that exhibit high intrinsic ductility.

36 MATERIALS SCIENCE↗