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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Yield strength prediction of high-entropy alloys using machine learning

Yield strength at high temperature is an important parameter in the design and application of high entropy alloys (HEAs). However, the experimental measurement of yield strength at high temperature is quite costly, complicated, and time-consuming. Therefore, it is essential to identify and apply a robust method for the accurate prediction of yield strength at high temperature from the available experimental and simulation data. In this study, for the first time, a machine learning (ML) method based on the regression technique of random forest (RF) regressor is used to predict the yield strength of HEAs at the desired temperature. Further, the yield strengths of MoNbTaTiW and HfMoNbTaTiZr at 800 °C and 1200 °C, are predicted using the RF regressor model. We find that the results are consistent with the experimental reports, showing that the RF regressor model predicts the yield strength of HEAs at the desired temperatures with high accuracy.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Ultrafine-grained Fe-TiB 2 high-modulus nanocomposite steel with high strength and isotropic mechanical properties by laser powder bed fusion

Fe-TiB 2 metal matrix composite, also called high-modulus steels (HMSs), are of great interest for applications in fuel-efficient transportation infrastructure, aerospace, and wear industries due to their high specific stiffness and yield strength. However, conventional cast Fe-TiB 2 HMSs often contain coarse and sharp-edged TiB 2 particles which easily trigger premature cracking during loading. Here, we synthesized a Fe-TiB 2 nanocomposite HMS via laser powder bed fusion (LPBF) additive manufacturing of mixed micro-sized powders of Fe, Ti, and Fe 2 B. We investigated the microstructure formation and mechanical behavior of the Fe-TiB 2 HMS. We found that in situ chemical reaction of Ti and Fe 2 B enables the formation of TiB 2 particles at nanoscale during rapid solidification of LPBF. These nanoscale TiB 2 particles can serve as heterogeneous nucleation sites and promote the formation of ultrafine and equiaxed α-Fe grains with random crystallographic textures, which differ from many other additively manufactured (AM) metal alloys characteristic of strong crystallographic textures. As such, isotropic mechanical properties were achieved in the AM Fe-TiB 2 nanocomposite HMS with a high elastic modulus of ~ 240 GPa, an exceptional yield strength of ~ 1450 MPa, and a large plasticity of ~ 20% under compression. Quantitative analysis reveals that the high yield strength primarily originates from strengthening contributions of the ultrafine grains with an average grain size of ~450 nm, the nanoscale TiB 2 reinforcing particles of 20–180 nm, and a high density of printing-induced dislocations of the order of 10 15 m –2 . In situ synchrotron high-energy X-ray diffraction unveils the load partitioning from the softer α-Fe matrix to the stiffer and stronger TiB 2 nanoparticles, contributing to the sustained strain hardening during compression. Our work not only provides a general pathway for achieving high-performance metal matrix nanocomposites by in situ chemical reaction and precipitation of ceramic nanoparticles during additive manufacturing, but also offers mechanistic insights into the deformation mechanism of nanoparticle-reinforced HMS composites.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Model Parameter Development for Complex Materials: Species-Specific Diffusion Barriers in 316 Stainless Steel from Systematic DFT Calculations

Vacancy-mediated diffusion barriers in 316 stainless steel have been systematically calculated using density functional theory to provide essential parameters for mesoscale microstructure evolution models. A statistical sampling approach employing 210 nudged elastic band calculations across multiple special quasi-random structures captures the effects of local chemical environments in this concentrated alloy. The computational methodology addresses challenges specific to chemically disordered systems, including proper magnetic treatment throughout multi-step calculations and validation against experimental structural properties. The calculated activation barriers reveal clear species-dependent diffusion behavior with the hierarchy Ni >> Fe ˜ Cr >> Mo. Nickel exhibits the highest barriers (0.74–1.31 eV, mean 1.045 eV), confirming its role as the slowest-diffusing major component. Iron and chromium show similar moderate barriers averaging 0.587 eV and 0.522 eV, respectively. Remarkably, molybdenum demonstrates exceptionally low barriers (0.12–0.28 eV, mean 0.194 eV), suggesting much higher mobility than previously recognized and potentially significant implications for precipitation kinetics and microstructure evolution. The barrier ranges remain consistent across different 316 SS compositions, supporting parameter transferability for modeling applications. The overall mean barrier of 0.64 eV provides a practical approximation for phase field simulations, while species-specific values enable detailed treatments of diffusion-controlled processes. This systematic approach establishes a validated framework for generating diffusion parameters in other concentrated alloys where experimental data are limited, while providing the first systematic set of species-specific barriers for predictive modeling of 316 stainless steel microstructure evolution.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Ab initio property predictions of quinary solid solutions using small binary cells

The Set of Small Ordered Structures (SSOS) approach is an ab initio technique for modelling random solid solutions in which many small structures are averaged so that their correlation functions match those of a desired composition. SSOS has been shown to be effective in reducing the cost of density functional theory calculations relative to other well-known techniques such as cluster expansions and special quasirandom structures for modelling solid solutions. Here in this work, we demonstrate that SSOS’s can be constructed using cells with only a subset of elements while still accurately modelling multi-component systems. Specifically, we show that small binary cells can effectively model two quinary high entropy alloys – NbTaTiHfZr and MoNbTaVW – accurately capturing properties such as formation energy, lattice parameters, elastic constants, and root-mean-square atomic displacements. Overall, this insight is useful for those looking to construct databases of such small structures for predicting the properties of multi-component solid solutions, as it greatly decreases the number of structures that needs to be considered.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Grain Boundary Segregation Suppresses Local Short‐Range Ordering in Nanocrystalline High‐Entropy Alloys

Multi-principal-element alloys like high-entropy alloys (HEAs) have potential applications in many engineering fields due to their unique mechanical/functional properties. While HEAs are generally considered random solid solutions, recent studies revealed that they are prone to short-range-ordering (SRO) due to the complex multi-pair-wise interactions among the constituent elements. Meanwhile, SROs' evolution can sometimes be deleterious, and it is necessary to have control over their evolution. Examining the AlCoCrFe-Zr model alloy, long-range ordering occurs following the expectation of enthalpic predictions. Advanced characterization techniques—transmission electron microscopy, high-energy synchrotron X-ray diffraction/pair distribution function, and atom probe tomography, reveal that SRO is suppressed in as-milled and GB-decorated NC-(AlCoCrFe)100-xZrx (x = 0–1.5 atomic %). Warren-Cowley coefficient calculations are further used to validate the suppression of SRO. Besides the low segregation enthalpies of Cr, Fe, and Zr, and the high-mixing enthalpy of Cr and Fe, the short diffusion path to GBs due to high-GB density in the NC-HEAs and the higher energy state of the GBs than the matrix promotes GB-segregation that further alters the matrix chemistry and consequently disfavors SRO formation within the matrix. Despite the GB-segregation of Cr, Fe, and Zr, the matrices and GBs remain in a random solid solution.

36 MATERIALS SCIENCE↗
DOE OSTI2020

High Thermoelectric Performance in the New Cubic Semiconductor AgSnSbSe 3 by High-Entropy Engineering

We investigate the structural and physical properties of the AgSn m SbSe m+2 system with m = 1-20 (i.e., SnSe matrix and ~5-50% AgSbSe 2 ) from atomic, nano, and macro length scales. We find the 50:50 composition, with m = 1 (i.e., AgSnSbSe 3 ), forms a stable cation-disordered cubic rock-salt p-type semiconductor with a special multi-peak electronic valence band structure. AgSnSbSe 3 has an intrinsically low lattice thermal conductivity of ~0.47 W m -1 K -1 at 673 K owing to the synergy of cation disorder, phonon anharmonicity, low phonon velocity, and low-frequency optical modes. Furthermore, Te alloying on Se sites creates a quinary high-entropy NaCl-type solid solution AgSnSbSe 3-x Te x with randomly disordered cations and anions. The extra point defects and lattice dislocations lead to glass-like lattice thermal conductivities of ~0.32 W m -1 K -1 at 723 K and higher hole carrier concentration than AgSnSbSe 3 . Concurrently, the Te alloying promotes greater convergence of the multiple valence band maxima in AgSnSbSe 1.5 Te 1.5 , the composition with the highest configurational entropy. Facilitated by these favorable modifications, we achieve a high average power factor of ~9.54 μW cm -1 K -2 (400-773 K), a peak thermoelectric figure of merit ZT of 1.14 at 723 K, and a high average ZT of ~1.0 over a wide temperature range of 400-773 K in AgSnSbSe 1.5 Te 1.5 .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Atomic arrangements in an amorphous CoFeB ribbon extracted via an analysis of radial distribution functions

Here we discuss the atomic structure of amorphous ferromagnetic FeCoB alloys, which are used widely in spintronics applications. Specifically, we obtain the pair-distribution functions for various atomic pairs based on high-energy x-ray diffraction data taken from an amorphous Co 20 Fe 61 B 19 specimen. We start our reverse Monte Carlo cycles to determine the disordered structure with a two-phase model in which a small amount of cobalt is mixed with Fe 23 B 6 as a second phase. The structure of the alloy is found to be heterogeneous, where the boron atoms drive disorder through the random occupation of the atomic network. Our analysis also indicates the presence of small cobalt clusters that are embedded in the iron matrix and percolating the latter throughout the structure. This morphology can explain the enhanced spin polarization observed in amorphous magnetic materials.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Short-range order and longer-range disorder revealed in germanium–tin alloy thin films by extended x-ray absorption fine structure analysis

Short-range order (SRO) in semiconductor alloys, a relatively under-studied structural phenomenon in which local atomic arrangements differ from those of a random solid solution, is investigated in molecular beam epitaxy (MBE)-grown GeSn thin films. A novel preparation technique is used to pattern these films into microscale ribbons that are released from the substrate for extended x-ray absorption fine structure (EXAFS) analysis. The results indicate a strong SRO in which the first shell around Sn atoms is greatly denuded of Sn atoms relative to the nominal atomic composition of the alloy. This effect is more pronounced than that observed recently in GeSn nanowires grown by chemical vapor deposition. Additionally, the presence of a longer-range disorder detected by EXAFS analysis in the shells of atoms more distant from the absorbers is indicative of the defects and inhomogeneous strain present in the MBE-grown films. The evident existence of the SRO in GeSn alloys deposited by different growth methods and in different strain states suggests that SRO is a general phenomenon in the thin films of this metastable solid solution.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

Neural network kinetics for exploring diffusion multiplicity and chemical ordering in compositionally complex materials

Diffusion involving atom transport from one location to another governs many important processes and behaviors such as precipitation and phase nucleation. The inherent chemical complexity in compositionally complex materials poses challenges for modeling atomic diffusion and the resulting formation of chemically ordered structures. Here, we introduce a neural network kinetics (NNK) scheme that predicts and simulates diffusion-induced chemical and structural evolution in complex concentrated chemical environments. The framework is grounded on efficient on-lattice structure and chemistry representation combined with artificial neural networks, enabling precise prediction of all path-dependent migration barriers and individual atom jumps. To demonstrate the method, we study the temperature-dependent local chemical ordering in a refractory NbMoTa alloy and reveal a critical temperature at which the B2 order reaches a maximum. The atomic jump randomness map exhibits the highest diffusion heterogeneity (multiplicity) in the vicinity of this characteristic temperature, which is closely related to chemical ordering and B2 structure formation. The scalable NNK framework provides a promising new avenue to exploring diffusion-related properties in the vast compositional space within which extraordinary properties are hidden.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Static and shock compression studies of eutectic high-entropy alloy AlCoCrFeNi 2.1 to ultrahigh pressures

The high-entropy alloy with composition AlCoCrFeNi 2.1 , additively manufactured with the laser powder-bed fusion technique, has a far-from-equilibrium BCC/FCC eutectic nanolamellar structure. We studied the high-pressure response of this alloy under both static compression and high-strain rate shock compression. The response to static compression using a diamond anvil cell was studied at pressures up to 302 GPa with synchrotron x-ray diffraction at the advanced photon source. The high-pressure FCC-only phase of the EHEA previously observed by Pope et al. [AIP Adv. 13, 035124 (2023)] is found to be stable up to the highest pressure achieved in this study with a volume compression of V/V 0 = 0.587 at ambient temperature. The shock experiments were performed by using GEKKO XII lasers at the Institute of Laser Engineering, Osaka University. The principal Hugoniot equation-of-state of the EHEA was measured up to a pressure of 515 GPa and a compression of V/V 0 = 0.613. Additionally, the thermal equation of state of the EHEA was measured up to 6.2 GPa and 1623 K using a large-volume Paris–Edinburgh cell to obtain the temperature dependence of bulk modulus and thermal expansion coefficients. The melting temperature for EHEA AlCoCrFeNi 2.1 at a pressure of 5.6 GPa was measured to be 1648 ± 25 K. These results can be used to refine stochastic (or special) quasi-random structure (SQS) models for high-pressure high-temperature behavior of high-entropy alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Accelerating computational modeling and design of high-entropy alloys

High-entropy alloys, with N elements and compositions {$c_{ν = 1,N}$} in competing crystal structures, have large design spaces for unique chemical and mechanical properties. In this work, to enable computational design, we use a metaheuristic hybrid Cuckoo search (CS) to construct alloy configurational models on the fly that have targeted atomic site and pair probabilities on arbitrary crystal lattices, given by supercell random approximates (SCRAPs) with S sites. Our Hybrid CS permits efficient global solutions for large, discrete combinatorial optimization that scale linearly in a number of parallel processors, and linearly in sites S for SCRAPs. For example, a four-element, 128-site SCRAP is found in seconds—a more than 13,000-fold reduction over current strategies. Our method thus enables computational alloy design that is currently impractical. We qualify the models and showcase application to real alloys with targeted atomic short-range order. Being problem-agnostic, our Hybrid CS offers potential applications in diverse fields.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Machine Learning Prediction of the Critical Cooling Rate for Metallic Glasses from Expanded Datasets and Elemental Features

In this study, we use a random forest (RF) model to predict the critical cooling rate (R C ) for glass formation of various alloys from features of their constituent elements. The RF model was trained on a database that integrates multiple sources of direct and indirect R C data for metallic glasses to expand the directly measured R C database of less than 100 values to a training set of over 2000 values. The model error on 5-fold cross-validation (CV) is 0.66 orders of magnitude in K/s. The error on leave-out-one-group CV on alloy system groups is 0.59 log units in K/s when the target alloy constituents appear more than 500 times in training data. Using this model, we make predictions for the set of compositions with melt-spun glasses in the database and for the full set of quaternary alloys that have constituents which appear more than 500 times in training data. These predictions identify a number of potential new bulk metallic glass systems for future study, but the model is most useful for the identification of alloy systems likely to contain good glass formers rather than detailed discovery of bulk glass composition regions within known glassy systems.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Evidence of ordering in Cu-Ni alloys from experimental electronic entropy measurements

Phase diagrams exhibiting extended solid-solution and lenslike melting are often reproduced using ideal solutions, where ideal mixing considers a fully random configurational entropy of mixing. In the field of irreversible thermodynamics, experimental measurements of the composition variation of high-temperature electronic transport and molten-state properties suggest, however, a strong role for short-range atomic ordering in these systems. Herein, measurements of the thermopower and resistivity are reported for Cu-Ni solid solutions as a function of temperature and composition. The electronic transport properties were interpreted with an irreversible thermodynamic framework, revealing a large electronic contribution to the entropy of mixing. By considering a cluster model for the configurational entropy that uses the electronic contribution to inform the existence of ordered associates, we rationalize such a contribution of the electronic entropy with the ideal entropy of mixing commonly used to model such systems. In conclusion, these results suggest that the short-range order of the atoms plays a significant role in both solid and liquid states, even when there are no dominant intermetallic compounds in these alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Prediction of defect properties in concentrated solid solutions using a Langmuir-like model

The alleged existence of sluggish diffusion in high-entropy alloys has drawn controversy. In high-entropy alloys and, in general, in all solids, transport properties are controlled by point defect concentration, which must be known before performing atomistic simulations to compute transport coefficients. In this work, we present a general Langmuir-like model for defect concentration in an arbitrarily complex solid solution and apply this model to generate expressions for concentrations of vacancies and small interstitial atoms. We then calculate the vacancy concentration as a function of temperature in the equiatomic CoNiCrFeMn and FeAl alloys with modified embedded-atom-method potentials for various chemical orderings, showing there is no clear correlation between vacancy thermodynamics and chemical ordering in the CoNiCrFeMn alloy, but clear systematic patterns for FeAl. We believe this is due to the high stability of disordered, random, and ordered intermetallic phases, respectively, in the CoNiCrFeMn and FeAl systems. Finally, this work provides future avenues to the prediction of thermal interstitials and vacancies in solid solutions, which is necessary for models of nonequilibrium behavior of solid solutions.

composition↗
DOE OSTI2020

Hydrogen Adsorption on Ordered and Disordered Pt-Ni Alloys

The bulk properties and chemical reactivity of disordered Pt-Ni alloys in the A1 ( fcc ) structure are investigated herein using different methods: Virtual Crystal Approximation (VCA), Korringa–Kohn–Rostoker Coherent Potential Approximation (KKR-CPA), and large explicit supercells generated using Super-Cell Random Approximates (SCRAPs). While VCA predicts lattice constants that closely follow Vegard’s law, the large supercells and KKR-CPA predict lattice constants that are consistently larger than Vegard’s law. KKR-CPA results closely agree with those from the large supercells for the disordered alloys, producing similar projected density of states and magnetic moment across the composition range. For instance, while VCA predicts the disordered alloys to be non-magnetic at a Pt concentration ( x Pt ) ≥ 0.5, KKR-CPA and SCRAPs predict the disordered alloys to remain ferromagnetic to higher Pt concentrations. As x Pt decreases, the adsorption of H becomes more exothermic on bulk-terminated (111) surfaces but less exothermic on Pt monolayer-terminated (111) surfaces due largely to strain effects. (111) surfaces cut from the large supercells predict average H adsorption energies on the disordered alloys similar to those on the ordered phases of the same compositions, while VCA predicts H adsorption to be more exothermic.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Statistics of the NiCoCr medium-entropy alloy: Novel aspects of an old puzzle

Abstract We study the K -state phenomenon in the NiCoCr medium-entropy alloy using first-principles techniques jointly with the efficient Wang–Landau Monte Carlo and simulated annealing algorithms. Our theoretical results successfully explain the existence of the peak around 940 K in the experimental specific heat curve that characterizes the K -state phenomenon and give a fine picture of its atomic origin. The peak is caused by the maximum change of the local configurations characterized by the short-range-order (SRO) parameters at that temperature. The maximum change in SRO parameters is dominated by the nearest-neighbor interactions of atoms but substantially tuned by the many-body interactions. One surprising aspect revealed by the reciprocal-space SRO parameters is that the Ni–Co pair distribution is not random even above the ordering transition temperature, dramatically different from Ni–Cr and Co–Cr, indicating the system cannot be treated as a pseudo binary alloy. This prototypical example shows the complicated nature of multicomponent alloys, different from binary alloys. Our methods can be directly used to study the important K -state phenomenon observed in a number of other composition-concentrated alloys regardless of their number of components.

Pei, Zongrui (ORCID:0000000307484629)↗
DOE OSTI2022

Ab initio approaches to high-entropy alloys: a comparison of CPA, SQS, and supercell methods

In this work, we present a comparative study of different modeling approaches to the electronic properties of the Hf 0.05 Nb 0.05 Ta 0.8 Ti 0.05 Zr 0.05 high-entropy alloy. Common to our modeling is the methodology to compute the one-particle Green’s function in the framework of density functional theory. We demonstrate that the special quasi-random structures modeling and the supercell, i.e., the locally self-consistent multiple-scattering methods, provide very similar results for the ground state properties such as the spectral function (density of states) and the equilibrium lattice parameter. To reconcile the multiple-scattering single-site coherent potential approximation with the real space supercell methods, we included the effect of screening of the net charges of the alloy components. Based on the analysis of the total energy and spectral functions computed within the density functional theory, we found no signature for the long-range or local magnetic moments formation in the Hf 0.05 Nb 0.05 Ta 0.8 Ti 0.05 Zr 0.05 high-entropy alloy; instead, we find possible superconductivity below ~9K.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Unraveling the dislocation–precipitate interactions in high-entropy alloys

The precipitates play a significant role in not only enhancing the strength, but also maintaining the high toughness in alloys. However, the interactions of the nanoscale precipitates with dislocations in the high entropy alloys (HEAs) are difficult to observe directly by in-situ TEM experiments due to the limits of the resolution and time. Here, using atomic simulations we report the synergistic strengthening of the coherent precipitate and atomic-scale lattice distortion in the HEAs at cryogenic/elevated temperatures. The effects of temperature, chemical disorder, precipitate spacing, precipitate size, elemental segregation, and dislocation-cutting number on the critical stress for the dislocation to overcome a row of precipitates are studied. A random stacking fault energy landscape along the slip plane, the lattice distortion at different temperatures, and the interface/surface energy at various crystallographic orientations are obtained. Compared with the traditional metals and alloys, HEAs have the severe atomic-scale lattice distortions to generate the local high tensile/compressive stress fields. This complex stress causes the dislocation line to bend, and thus improves the dislocation slip resistance, resulting in the strong solid-solution strengthening. The stacking fault strengthening induced by the obvious difference of the stacking fault energies between the HEA matrix and precipitate (within the inner of the HEA matrix), and the formation of the antiphase domain boundary contribute to the high strength. The precipitate embedded by the solute atoms produces the strong lattice distortion to enhance the dislocation slip resistance at high temperatures. Hence, the current results provide the mechanistic insight into the phenomenon that the coherent precipitate combined with the severe atomic-scale lattice distortion can enhance the strength at cryogenic/elevated temperatures to further broaden the scope of applications of advanced HEAs.

36 MATERIALS SCIENCE↗