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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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At least 1,009 records · Page 56
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Effects of local compositional and structural disorder on vacancy formation in entropy-stabilized oxides from first-principles

Entropic stabilization has evolved into a strategy to create new oxide materials and realize novel functional properties engineered through the alloy composition. Achieving an atomistic understanding of these properties to enable their design, however, has been challenging due to the local compositional and structural disorder that underlies their fundamental structure-property relationships. Here, we combine high-throughput atomistic calculations and linear regression algorithms to investigate the role of local configurational and structural disorder on the thermodynamics of vacancy formation in (MgCoNiCuZn)O-based entropy-stabilized oxides (ESOs) and their influence on the electrical properties. We find that the cation-vacancy formation energies decrease with increasing local tensile strain caused by the deviation of the bond lengths in ESOs from the equilibrium bond length in the binary oxides. The oxygen-vacancy formation strongly depends on structural distortions associated with the local configuration of chemical species. Vacancies in ESOs exhibit deep thermodynamic transition levels that inhibit electrical conduction. By applying the charge-neutrality condition, we determine that the equilibrium concentrations of both oxygen and cation vacancies increase with increasing Cu mole fraction. Our results demonstrate that tuning the local chemistry and associated structural distortions by varying alloy composition acts an engineering principle that enables controlled defect formation in multi-component alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A deep neural network regressor for phase constitution estimation in the high entropy alloy system Al-Co-Cr-Fe-Mn-Nb-Ni

High Entropy Alloys (HEAs) are composed of more than one principal element and constitute a major paradigm in metals research. The HEA space is vast and an exhaustive exploration is improbable. Therefore, a thorough estimation of the phases present in the HEA is of paramount importance for alloy design. Machine Learning presents a feasible and non-expensive method for predicting possible new HEAs on-the-fly. A deep neural network (DNN) model for the elemental system of: Mn, Ni, Fe, Al, Cr, Nb, and Co is developed using a dataset generated by high-throughput computational thermodynamic calculations using Thermo-Calc. The features list used for the neural network is developed based on literature and freely available databases. A feature significance analysis matches the reported HEAs phase constitution trends on elemental properties and further expands it by providing so far-overlooked features. The final regressor has a coefficient of determination ( r 2 ) greater than 0.96 for identifying the most recurrent phases and the functionality is tested by running optimization tasks that simulate those required in alloy design. The DNN developed constitutes an example of an emulator that can be used in fast, real-time materials discovery/design tasks.

36 MATERIALS SCIENCE↗
DOE OSTI2025

High entropy powering green energy: hydrogen, batteries, electronics, and catalysis

A reformation in energy is underway to replace fossil fuels with renewable sources, driven by the development of new, robust, and multi-functional materials. High-entropy materials (HEMs) have emerged as promising candidates for various green energy applications, having unusual chemistries that give rise to remarkable functionalities. This review examines recent innovations in HEMs, focusing on hydrogen generation/storage, fuel cells, batteries, semiconductors/electronics, and catalysis—where HEMs have demonstrated the ability to outperform state-of-the-art materials. We present new master plots that illustrate the superior performance of HEMs compared to conventional systems for hydrogen generation/storage and heat-to-electricity conversion. We highlight the role of computational methods, such as density functional theory and machine learning, in accelerating the discovery and optimization of HEMs. The review also presents current challenges and proposes future directions for the field. We emphasize the need for continued integration of modeling, data, and experiments to investigate and leverage the underlying mechanisms of the HEMs that are powering progress in sustainable energy.

batteries↗
DOE OSTI2025

Information-entropy-driven generation of material-agnostic datasets for machine-learning interatomic potentials

In contrast to their empirical counterparts, machine-learning interatomic potentials (MLIAPs) promise to deliver near-quantum accuracy over broad regions of configuration space. However, due to their generic functional forms and extreme flexibility, they can catastrophically fail to capture the properties of novel, out-of-sample configurations, making the quality of the training set a determining factor, especially when investigating materials under extreme conditions. We propose a novel automated dataset generation method based on the maximization of the information entropy of the feature distribution, aiming at an extremely broad coverage of the configuration space in a way that is agnostic to the properties of specific target materials. The ability of the dataset to capture unique material properties is demonstrated on a range of unary materials, including elements with the FCC (Al), BCC (W), HCP (Be, Re and Os), graphite (C), and trigonal (Sb, Te) ground states. MLIAPs trained to this dataset are shown to be accurate over a range of application-relevant metrics, as well as extremely robust over very broad swaths of configurations space, even without dataset fine-tuning or hyper-parameter optimization, making the approach extremely attractive to rapidly and autonomously develop general-purpose MLIAPs suitable for simulations in extreme conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Dynamic substrate reactions during room temperature heavy ion irradiation of CoCrCuFeNi high entropy alloy thin films

Abstract High entropy alloys (HEAs) are promising materials for various applications including nuclear reactor environments. Thus, understanding their behavior under irradiation and exposure to different environments is important. Here, two sets of near-equiatomic CoCrCuFeNi thin films grown on either SiO 2 /Si or Si substrates were irradiated at room temperature with 11.5 MeV Au ions, providing similar behavior to exposure to inert versus corrosion environments. The film grown on SiO 2 had relatively minimal change up to peak damage levels above 500 dpa, while the film grown on Si began intermixing at the substrate–film interface at peak doses of 0.1 dpa before transforming into a multi-silicide film at higher doses, all at room temperature with minimal thermal diffusion. The primary mechanism is radiation-enhanced diffusion via the inverse Kirkendall and solute drag effects. The results highlight how composition and environmental exposure affect the stability of HEAs under radiation and give insights into controlling these behaviors.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Proximity to a critical point driven by electronic entropy in URu2Si2

Abstract The strongly correlated actinide metal URu 2 Si 2 exhibits a mean field-like second order phase transition at T o ≈ 17 K, yet lacks definitive signatures of a broken symmetry. Meanwhile, various experiments have also shown the electronic energy gap to closely resemble that resulting from hybridization between conduction electron and 5 f -electron states. We argue here, using thermodynamic measurements, that the above seemingly incompatible observations can be jointly understood by way of proximity to an entropy-driven critical point, in which the latent heat of a valence-type electronic instability is quenched by thermal excitations across a gap, driving the transition second order. Salient features of such a transition include a robust gap spanning highly degenerate features in the electronic density of states, that is weakly (if at all) suppressed by temperature on approaching T o , and an elliptical phase boundary in magnetic field and temperature that is Pauli paramagnetically limited at its critical magnetic field.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Engineering the entropy-driven free-energy landscape of a dynamic nanoporous protein assembly

De novo design and construction of stimuli-responsive protein assemblies that predictably switch between discrete conformational states remains an essential but highly challenging goal in biomolecular design. We previously reported synthetic, two dimensional protein lattices self-assembled via disulfide bonding interactions, which endows them with a unique capacity to undergo coherent conformational changes without losing crystalline order. Here, we carried out all-atom molecular dynamics simulations to map the free-energy landscape of these lattices, validated this landscape through extensive structural characterization by electron microscopy and established that it is predominantly governed by solvent reorganization entropy. Subsequent redesign of the protein surface with conditionally repulsive electrostatic interactions enabled us to predictably perturb the free-energy landscape and obtain a new protein lattice whose conformational dynamics can be chemically and mechanically toggled between three different states with varying porosities and molecular densities.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Non-equilibrium entropy production and information dissipation in a non-Markovian quantum dot

This study measures trajectory-level entropy production and information dissipation in a driven, non-Markovian quantum dot using time-resolved optical dynamics and machine-learning-based analysis. Although not a 2D-material system, it is relevant because it demonstrates quantitative extraction of nonequilibrium dynamics from nanoscale optical fluctuations, which is conceptually connected to the proposed studies of transient charge and spin dynamics at interfaces.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Enhanced strength and ductility in a high-entropy alloy via ordered oxygen complexes

Oxygen, one of the most abundant elements on Earth, often forms an undesired interstitial impurity or ceramic phase (such as an oxide particle) in metallic materials. Even when it adds strength, oxygen doping renders metals brittle. Here, in this study, we show that oxygen can take the form of ordered oxygen complexes, a state in between oxide particles and frequently occurring random interstitials. Unlike traditional interstitial strengthening, such ordered interstitial complexes lead to unprecedented enhancement in both strength and ductility in compositionally complex solid solutions, the so-called high-entropy alloys (HEAs). The tensile strength is enhanced (by 48.5 ± 1.8 per cent) and ductility is substantially improved (by 95.2 ± 8.1 per cent) when doping a model TiZrHfNb HEA with 2.0 atomic per cent oxygen, thus breaking the long-standing strength–ductility trade-off. The oxygen complexes are ordered nanoscale regions within the HEA characterized by (O, Zr, Ti)-rich atomic complexes whose formation is promoted by the existence of chemical short-range ordering among some of the substitutional matrix elements in the HEAs. Carbon has been reported to improve strength and ductility simultaneously in face-centred cubic HEAs, by lowering the stacking fault energy and increasing the lattice friction stress. By contrast, the ordered interstitial complexes described here change the dislocation shear mode from planar slip to wavy slip, and promote double cross-slip and thus dislocation multiplication through the formation of Frank–Read sources (a mechanism explaining the generation of multiple dislocations) during deformation. This ordered interstitial complex-mediated strain-hardening mechanism should be particularly useful in Ti-, Zr- and Hf-containing alloys, in which interstitial elements are highly undesirable owing to their embrittlement effects, and in alloys where tuning the stacking fault energy and exploiting athermal transformations do not lead to property enhancement. These results provide insight into the role of interstitial solid solutions and associated ordering strengthening mechanisms in metallic materials.

Lei, Zhifeng↗
DOE OSTI2022

Strong yet ductile nanolamellar high-entropy alloys by additive manufacturing

Additive manufacturing produces net-shaped components layer by layer for engineering applications. The additive manufacture of metal alloys by laser powder bed fusion (L-PBF) involves large temperature gradients and rapid cooling, which enables microstructural refinement at the nanoscale to achieve high strength. However, high-strength nanostructured alloys produced by laser additive manufacturing often have limited ductility. In this work, we use L-PBF to print dual-phase nanolamellar high-entropy alloys (HEAs) of AlCoCrFeNi 2.1 that exhibit a combination of a high yield strength of about 1.3 gigapascals and a large uniform elongation of about 14 percent, which surpasses those of other state-of-the-art additively manufactured metal alloys. The high yield strength stems from the strong strengthening effects of the dual-phase structures that consist of alternating face-centred cubic and body-centred cubic nanolamellae; the body-centred cubic nanolamellae exhibit higher strengths and higher hardening rates than the face-centred cubic nanolamellae. The large tensile ductility arises owing to the high work-hardening capability of the as-printed hierarchical microstructures in the form of dual-phase nanolamellae embedded in microscale eutectic colonies, which have nearly random orientations to promote isotropic mechanical properties. The mechanistic insights into the deformation behaviour of additively manufactured HEAs have broad implications for the development of hierarchical, dual- and multi-phase, nanostructured alloys with exceptional mechanical properties.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Corrosion and Thermal Stability of CrMnFeNi High Entropy Alloy in Molten FLiBe Salt

The corrosion behavior of the FCC Cr 18 Mn 27 Fe 27.5 Ni 27.5 high entropy alloy (HEA) after exposure to molten FLiBe salt at 700 °C for 1000 hours, has been investigated. Results show that the HEA lost a higher mass compared to the reference 316 H stainless steel due to the dissolution of Mn into the molten salt. The loss of Mn from the alloy appeared to discourage the dissolution of Cr in the molten fluoride salts which is widely recognized as the mechanism of corrosion degradation. Thermal exposure at 700 °C for 1000 hours also led to the precipitation of an additional BCC phase Cr 67 Fe 13 Mn 18.5 Ni 1.5 , which was confirmed by CALPHAD predictions.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Superconductivity in a uranium containing high entropy alloy

Abstract High entropy alloys (HEA) are an unusual class of materials where mixtures of elements are stochastically arrayed on a simple crystalline lattice. These systems exhibit remarkable functionality, often along several distinct axes: e.g., the examples [TaNb] 1-x (TiZrHf) x are high strength and damage resistant refractory metals that also exhibit superconductivity with large upper critical fields. Here we report the discovery of an f -electron containing HEA, [TaNb] 0.31 (TiUHf) 0.69 , which is the first to include an actinide ion. Similar to the Zr-analogue, this material crystallizes in a body-centered cubic lattice with the lattice constant a = 3.41(1) Å and exhibits phonon mediated superconductivity with a transition temperatures T c ≈ 3.2 K and upper critical fields H c2 ≈ 6.4 T. These results expand this class of materials to include actinide elements, shows that superconductivity is robust in this sub-group, and opens the path towards leveraging HEAs as functional waste forms for a variety of radioisotopes.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Evidence of Inverse Hall-Petch Behavior and Low Friction and Wear in High Entropy Alloys

We present evidence of inverse Hall-Petch behavior for a single-phase high entropy alloy (CoCrFeMnNi) in ultra-high vacuum and show that it is associated with low friction coefficients (~0.3). Grain size measurements by STEM validate a recently proposed dynamic amorphization model that accurately predicts grain size-dependent shear strength in the inverse Hall-Petch regime. Wear rates in the initially soft (coarse grained) material were shown to be remarkably low (~10 –6 mm 3 /N-m), the lowest for any HEA tested in an inert environment where oxidation and the formation of mixed metal-oxide films is mitigated. The combined high wear resistance and low friction are linked to the formation of an ultra-nanocrystalline near-surface layer. The dynamic amorphization model was also used to predict an average high angle grain boundary energy (0.87 J/m 2 ). This value was used to explain cavitation-induced nanoporosity found in the highly deformed surface layer, a phenomenon that has been linked to superplasticity.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Tensile deformation behavior of twist grain boundaries in CoCrFeMnNi high entropy alloy bicrystals

High entropy alloys (HEA) are a class of materials that consist of multiple elemental species in similar concentrations. The use of elements in far from dilute concentrations introduces a multi-dimensional composition design space by which the properties of metallic systems can be tailored. While the mechanical behavior of HEAs has been the subject of active research recently, the role of grain boundaries (GBs) in their deformation behavior remains poorly understood. Motivated by recent experiments on HEAs demonstrating that GBs act as nucleation sites for deformation twins, herein, we leverage atomistic simulations to construct a series of equiatomic CoCrFeMnNi HEA bicrystals with $\langle 110 \rangle$ and $\langle 111 \rangle$ symmetric twist GBs and examine their tensile behavior and underlying deformation mechanisms at 77 K. Simulation results reveal that plastic deformation proceeds by the nucleation of partial dislocations from GBs, which then grow with further loading by bowing into the bulk crystals leaving behind stacking faults. Variations in the nucleation stress exist as function of GB character, defined in this work by the twist angle. Our results provide future avenues to explore GBs as a microstructure design tool to develop HEAs with tailored properties.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Correlating work hardening with co-activation of stacking fault strengthening and transformation in a high entropy alloy using in-situ neutron diffraction

Transformation induced plasticity (TRIP) leads to enhancements in ductility in low stacking fault energy (SFE) alloys, however to achieve an unconventional increase in strength simultaneously, there must be barriers to dislocation motion. While stacking faults (SFs) contribute to strengthening by impeding dislocation motion, the contribution of SF strengthening to work hardening during deformation is not well understood; as compared to dislocation slip, twinning induced plasticity (TWIP) and TRIP. Thus, we used in-situ neutron diffraction to correlate SF strengthening to work hardening behavior in a low SFE Fe 40 Mn 20 Cr 15 Co 20 Si 5 (at%) high entropy alloy, SFE ~ 6.31 mJ m –2 . Cooperative activation of multiple mechanisms was indicated by increases in SF strengthening and γ-f.c.c. → ε-h.c.p. transformation leading to a simultaneous increase in strength and ductility. The present study demonstrates the application of in-situ, neutron or X-ray, diffraction techniques to correlating SF strengthening to work hardening.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Co-introduction of precipitate hardening and TRIP in a TWIP high-entropy alloy using friction stir alloying

Tuning deformation mechanisms is imperative to overcome the well-known strength-ductility paradigm. Twinning-induced plasticity (TWIP), transformation-induced plasticity (TRIP) and precipitate hardening have been investigated separately and have been altered to achieve exceptional strength or ductility in several alloy systems. In this study, we use a novel solid-state alloying method—friction stir alloying (FSA)—to tune the microstructure, and a composition of a TWIP high-entropy alloy by adding Ti, and thus activating site-specific deformation mechanisms that occur concomitantly in a single alloy. During the FSA process, grains of the as-cast face-centered cubic matrix were refined by high-temperature severe plastic deformation and, subsequently, a new alloy composition was obtained by dissolving Ti into the matrix. After annealing the FSA specimen at 900 °C, hard Ni–Ti rich precipitates formed to strengthen the alloy. An additional result was a Ni-depleted region in the vicinity of newly-formed precipitates. The reduction in Ni locally reduced the stacking fault energy, thus inducing TRIP-based deformation while the remaining matrix still deformed as a result of TWIP. Our current approach presents a novel microstructural architecture to design alloys, an approach that combines and optimizes local compositions such that multiple deformation mechanisms can be activated to enhance engineering properties.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Data-driven analysis and prediction of stable phases for high-entropy alloy design

High-entropy alloys (HEAs) represent a promising class of materials with exceptional structural and functional properties. However, their design and optimization pose challenges due to the large composition-phase space coupled with the complex and diverse nature of the phase formation dynamics. In this study, a data-driven approach that utilizes machine learning (ML) techniques to predict HEA phases and their composition-dependent phases is proposed. By employing a comprehensive dataset comprising 5692 experimental records encompassing 50 elements and 11 phase categories, we compare the performance of various ML models. Our analysis identifies the most influential features for accurate phase prediction. Furthermore, the class imbalance is addressed by employing data augmentation methods, raising the number of records to 1500 in each category, and ensuring a balanced representation of phase categories. The results show that XGBoost and Random Forest consistently outperform the other models, achieving 86% accuracy in predicting all phases. Additionally, this work provides an extensive analysis of HEA phase formers, showing the contributions of elements and features to the presence of specific phases. We also examine the impact of including different phases on ML model accuracy and feature significance. Notably, the findings underscore the need for ML model selection based on specific applications and desired predictions, as feature importance varies across models and phases. This study significantly advances the understanding of HEA phase formation, enabling targeted alloy design and fostering progress in the field of materials science.

36 MATERIALS SCIENCE↗
DOE OSTI2023

All-temperature zinc batteries with high-entropy aqueous electrolyte

Electrification of transportation and rising demand for grid energy storage continue to build momentum around batteries across the globe. However, the supply chain of Li-ion batteries is exposed to the increasing challenges of resourcing essential and scarce materials. Therefore, incentives to develop more sustainable battery chemistries are growing. Here, in this paper, we show an aqueous ZnCl 2 electrolyte with introduced LiCl as supporting salt. Once the electrolyte is optimized to Li 2 ZnCl 4 ∙9H 2 O, the assembled Zn–air battery can sustain stable cycling over the course of 800 hours at a current density of 0.4 mA cm -2 between -60 °C and +80 °C, with 100% Coulombic efficiency for Zn stripping/plating. Even at -60 °C, >80% of room-temperature power density can be retained. Advanced characterization and theoretical calculations reveal a high-entropy solvation structure that is responsible for the excellent performance. The strong acidity allows ZnCl 2 to accept donated Cl - ions to form ZnCl 4 2- anions, while water molecules remain within the free solvent network at low salt concentration or coordinate with Li ions. Our work suggests an effective strategy for the rational design of electrolytes that could enable next-generation Zn batteries.

25 ENERGY STORAGE↗