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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A new magnesium sheet alloy with high tensile properties and room-temperature formability

Lightweight sheet alloys with superior mechanical performance such as high strength, ductility and formability at room temperature (RT) are desirable for high volume automotive applications. However, ductility or formability of metallic alloys at RT are generally inversely related to strength, thereby making it difficult to optimize all three simultaneously. Here we design a new magnesium sheet alloy-ZAXME11100 (Mg-1.0Zn-1.0Al-0.5Ca-0.4Mn-0.2Ce, wt. pct.) via CALPHAD (CALculation of PHAse Diagram) modeling and experimental validation. This new sheet alloy offers an excellent RT formability with a high Index Erichsen (I.E.) value of 7.8 mm in a solution-treated condition (T4), due to its weak and split basal texture and fine grain structure. The new ZAXME 11100 alloy also shows a rapid age-hardening response during post-forming artificial aging treatment at 210 °C for 1hour (T6), resulting in a significant increase of yield strength from 159 MPa (T4) to 270 MPa (T6). The excellent combination of T4 ductility (31%), T4 formability (7.8 mm) and T6 yield strength (270 MPa) in this new magnesium alloy is comparable to that of common 6xxx series aluminum sheet alloys. Thus, this new magnesium sheet alloy is highly attractive for sheet applications in automotive and other industries.

36 MATERIALS SCIENCE↗
DOE OSTI2026

High-throughput validation of phase formability and simulation accuracy of Cantor alloys

High-throughput methods enable accelerated discovery of novel materials in complex systems such as high-entropy alloys, which exhibit intricate phase stability across vast compositional spaces. Computational approaches, including Density Functional Theory (DFT) and calculation of phase diagrams (CALPHAD), facilitate screening of phase formability as a function of composition and temperature. However, the integration of computational predictions with experimental validation remains challenging in high-throughput studies. In this work, we introduce a quantitative confidence metric to assess the agreement between predictions and experimental observations, providing a quantitative measure of the confidence of machine learning models trained on either DFT or CALPHAD input in accounting for experimental evidence. The experimental dataset was generated via high-throughput in-situ synchrotron X-ray diffraction on compositionally varied FeNiMnCr alloy libraries, heated from room temperature to ~1000 °C. Agreement between the observed and predicted phases was evaluated using either temperature-independent phase classification or a model that incorporates a temperature-dependent probability of phase formation. This integrated approach demonstrates where strong overall agreement between computation and experiment exists, while also identifying key discrepancies, particularly in FCC/BCC predictions at Mn-rich regions to inform future model refinement.

36 - MATERIALS SCIENCE↗
DOE OSTI2026

Predicting the High-Temperature Oxidation Response of Nickel Superalloys Using CALPHAD-Enhanced Machine Learning

Structural materials such as Ni-based superalloys used in high-temperature power cycles are routinely exposed to toxic environments including high temperature and pressure, aqueous and gas corrosion, etc. Here, we present a physics-informed machine learning approach to predict the oxidation response of diverse Ni-superalloys. First, a high-fidelity experimental dataset is curated from typical oxidation mass-change experiments in air, covering 25+ elements and different physical behavior such as parabolic growth, non-parabolic growth, and oxide spallation. Second, the dataset is featurized using thermophysical, chemical, and mechanical properties obtained from high-throughput CALPHAD calculations. Third, several machine learning models are developed to identify key features related to mass-change characteristics and model the mass-change curve for various alloys. Finally, the model is deployed to rapidly screen over a new composition space and down-select candidate alloys with high oxidation resistance for experimental validation.

CALPHAD↗
DOE OSTI2026

Effect of magneto-mechanical synergism in the process-structure correlation in Fe–C alloys: A phase-field modeling approach

Applied magnetic fields can alter phase equilibria and kinetics in steels; however, quantitatively resolving how magnetic, chemical, and elastic driving forces jointly influence the microstructure remains challenging. We develop a quantitative magneto-mechanically coupled phase-field model for the Fe–C system that couples a CALPHAD-based chemical free energy with demagnetization-field magnetostatics and microelasticity. Here, the model reproduces single- and multi-particle evolution during the α → γ inverse transformation at 1023 K under external fields up to 20 T, including ellipsoidal morphologies observed experimentally at 8 T. Chemically driven growth is isotropic; a magnetic interaction introduces an anisotropic driving force that elongates γ precipitates along the field into ellipsoids, while elastic coherency promotes faceting, yielding elongated cuboidal or “brick-like” particles under combined magneto-elastic coupling. Growth kinetics increase with C content, and decrease with field strength and misfit strain. Multi-particle simulations reveal dipolar interaction-mediated coalescence for field-parallel neighbors and ripening for field-perpendicular neighbors. Incorporating field-dependent diffusivity from experiment slows kinetics as expected; a first-principles-motivated anisotropic diffusivity correction is estimated to be small (<2%). These results establish a process-structure link for magnetically assisted heat treatments of Fe–C alloys and provide guidance for microstructure control via chemo-magneto-mechanical synergism.

Magnetic field↗
DOE OSTI2022

Thermophysical modeling of raw glaze liquidus temperature and viscosity

Raw glazes, which do not contain pre-melted glass frits, are widely used due to their low cost. The natural variability of the mineral compositions used in raw glazes affects their viscosity during firing. Molten glaze viscosity plays an important role in determining the final surface quality; therefore, it is important to determine how raw glaze composition and firing conditions affect viscosity. Thermophysical modeling provides a way to evaluate the liquidus temperature and viscosity of molten glazes as a function of composition and oxygen activity. Equilibrium phase volume fraction and composition of 242 raw glaze recipes were evaluated under oxidizing and reducing conditions. Undissolved or precipitation of solid phases explain the secondary flux transition from anti-fluxing to fluxing near the liquidus temperature. The liquidus temperature and viscosity of iron glazes decrease as a function of increasing iron content under a reducing atmosphere. The empirical probability distribution of molten glaze viscosity follows a lognormal distribution, with lead glazes having significantly lower viscosity compared with glazes without lead. Here, the peak firing temperature viscosity of lead-free glazes is near the working point of glass. Multiple linear regression analysis shows that the peak firing temperature viscosity and liquidus temperature significantly predict the viscosity factor.

36 MATERIALS SCIENCE↗
DOE OSTI2025

CALPHAD-based Bayesian optimization to accelerate alloy discovery for high-temperature applications

Two crucial properties influencing the performance of high-temperature alloys are coefficient of thermal expansion (CTE) and phase constitution. It is desirable to have alloys with low CTE, which reduces CTE mismatch with the surface oxide and the likelihood of oxide spallation. Reducing the amount of brittle intermetallic phases such as Sigma (σ) enhances alloy ductility and processability. Here, we propose a multi-objective Bayesian Optimization (BO) model to simultaneously minimize CTE (at an operational temperature of 1150 °C) and T σ (temperature when the Sigma phase completely dissolves in the metal matrix), properties which are obtained from high-throughput CALculation of PHAse Diagrams (CALPHAD). The model successfully identifies several alloys with CTE ≤ 2 × 10 –5 /K and T σ ≤ 500 °C by exploring just 7% of the nickel–chromium–cobalt–aluminum–iron (Ni–Cr–Co–Al–Fe) composition space. Such multi-objective alloy design frameworks can be used to inform additive manufacturing experiments and accelerate alloy discovery for high-temperature energy applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Computational Exploration of High Entropy Alloys as Promising Materials for Future Beam Windows

With the ever-increasing demand for high beam power, the currently used beam-intercepting devices (BIDs) such as targets, and beam windows may not be able to handle the high power required for future accelerator complexes or the lifetime may be reduced drastically. As beam power increases, the damage incurred by BIDs, including thermal shock, fatigue, and irradiation damage, also rises. Therefore, it is imperative to design materials that can withstand high beam power for longer lifetimes. High entropy alloys (HEAs) have emerged as potential alternative materials for designing next-generation BIDs. In this study, we primarily focus on materials for developing beam windows for next-generation accelerator complexes. We propose an integrated approach that combines various computational techniques to study and design new materials. Specifically, we use CALPHAD, density functional theory (DFT), and molecular dynamics (MD) to comprehensively investigate the defect properties of suitable HEAs, offering potential alternatives for future beam windows. We begin by scanning the extensive phase space provided by Cr-Mn-V-Ti-Al-Co HEAs, selecting 8 compositions after evaluating approximately 120,000 unique compositions using CALPHAD. We, then employ DFT-informed machine learning techniques to develop force-field parameters. Finally, MD simulations using these developed force-field parameters will be used to study the effects of radiation damage on the defect and mechanical properties of the selected alloys. This research explains the use of the CALPHAD approach and shows how critical modeling (DFT and MD) is in developing novel material such as HEAs. It also highlights the promising role of machine learning in this field. The results from this study will greatly improve the novel materials development to be used in next-generation accelerator components, leading to higher beam power and longer operational times of BIDs.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Directed energy deposition of radially functionally graded nickel based superalloys IN625 and IN738: Microstructure and mechanical properties

This study investigates the microstructure and mechanical properties of radially functionally graded (RFG) materials fabricated from nickel-based superalloys IN625 and IN738 using directed energy deposition (DED). RFG cylindrical deposits with radial gradients of IN625-IN738 and IN738-IN625 were successfully fabricated and confirmed to be crack-free through tailored composition grading, scan strategy and process parameters that minimized thermal gradients and solidification velocities. Microstructural analysis revealed distinct variations across the gradient, characterized by differences in dendrite arm spacing, microsegregation, and the formation of distinct γ' precipitates and carbide/boride phases (MC, M 6 C, M 2 C, and MB 2 ) in as-deposited (AD) conditions, which evolved into M 23 C 6 and M 2 B upon subsequent heat treatment (HT). EBSD showed predominant columnar grains with a [100] texture, while microhardness profiles demonstrated consistent variation across the gradients. Mechanical testing highlighted the synergy of the graded structure, combining IN738's strength with IN625's ductility, resulting in enhanced properties such as a yield strength of 1131 MPa, ultimate tensile strength of 1382 MPa, and elongation of 12 % for HT IN625-IN738 grading. Numerical simulations employing CALPHAD thermodynamic calculations and finite element thermal modeling are used to establish phase evolution and microstructural feature correlation with thermal profiles, aiding in the optimization of process-structure-property relationship.

Additive manufacturing↗
DOE OSTI2022

Liquid and Gamma-BCC Phase Equilibria in the Uranium-Niobium System: A Simple Thermodynamic Model for Phase Diagram and Other Predictions

A simple thermodynamic model for the uranium-niobium (U-Nb) system was created using a Calphad-type approach, circa 2002. Only the liquid and gamma-BCC phases were modeled, with their subregular solution mixing terms optimized by trial and error. The custom (user-defined) database file (.tdb) and experimental data file (.exp) are listed, both of which are compatible with Thermo-Calc software. This model is used to calculate the solidus, liquidus, miscibility gap on the U-Nb phase diagram and also the enthalpy of melting in the U-6Nb alloy.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

ORNL/Thermo4PFM

Library to compute equilibrium compositions and equal chemical potential compositions in binary and ternary alloys. This library supports thermodynamic data formulated according to CALPHAD (Computer Coupling of Phase Diagrams and Thermochemistry) models, as well as dilute binary alloys. One major aim is to solve the Kim-Kim-Suzuki (KKS) equations to obtain local compositions given a local mixture of two phases. These quantities are then used to evaluate the phase-field model driving force (right handside of time-evolution equation), as well as the energy density

Fattebert, Jean-Luc↗
DOE OSTI2022

High-Temperature Thermodynamics Modeling of Graphite

We present high-temperature thermodynamic properties for graphite from first-principles anharmonic theory. The ab initio electronic structure is obtained from density-functional theory coupled to a lattice dynamics method that is used to model anharmonic lattice vibrations. This combined approach produces free energies and specific heats for graphite that compare well with available experiments and results from models that empirically represent experimental data, such as CALPHAD. We show that anharmonic theory for the phonons is essential for accurate thermodynamic quantities above about 1000 K.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Development of Innovative Non-Concentrated Alloy (NCA) Fuel Cladding for Advanced Nuclear Reactors

As the nuclear energy sector advances toward next-generation reactors, the need for high-performance fuel cladding materials has become increasingly urgent. Traditional alloys like zirconium and stainless steel are reaching their performance limits under higher temperatures, more corrosive coolants, and extended irradiation. This report presents the development of a new class of fuel cladding materials based on Non-Concentrated Alloys (NCAs) ? multi-element systems designed to deliver enhanced mechanical strength, corrosion resistance, and radiation tolerance. Through a combination of computational modeling (Computer Coupling of Phase Diagrams and Thermochemistry: CALPHAD), simulation-guided alloy selection, and experimental fabrication via arc melting and spark plasma sintering (SPS), three strategic alloy design paths were explored: (1) FeCrAl-based NCAs, (2) refractory-lean neutron-efficient alloys, and (3) equimolar high-entropy compositions. Microstructural analysis confirmed the formation of stable body-centered cubic BCC_A2 phases, while mechanical testing demonstrated hardness values significantly exceeding those of conventional cladding materials. The results highlight the tunability of NCA systems and their potential for balancing strength and ductility ? a critical consideration for in-reactor performance. Looking forward, future work will focus on thermomechanical optimization, CALPHAD refinement, and benchmarking against industry standards to enable scalable deployment. This work not only advances the science of nuclear materials but also supports broader goals in nuclear safety, performance, and nuclear energy innovation.

36 - MATERIALS SCIENCE↗
DOE OSTI2023

Predicting Sintering Window of Binder Jet Additively Manufactured Parts Using a Coupled Data Analytics and CALPHAD Approach

Batch-to-batch variation in powder compositions for binder jet additive manufacturing (BJAM) can significantly deter defining an “ideal” sintering window for a given alloy. One way to overcome the problem is by running sintering experiments at various temperatures for each batch of the powder. However, such an approach increases the time required to achieve large-scale production of parts. The predictive capabilities of computational thermodynamic tools like CALPHAD can be leveraged to overcome the challenge, especially for binder jet additive manufacturing, since the process occurs under near-equilibrium conditions. However, calculating the sintering window using CALPHAD can be computationally expensive, considering many possible feedstock compositions within “specification”. Here, we generate high throughput CALPHAD data for nickel-based superalloys to develop machine learning models to predict the sintering window rapidly. The predictive capability of the models has been validated using published results on BJAM of Inconel 718 and 625. Further, validated models are lightweight and can be deployed in an industrial setting to get sintering window in an accelerated manner.

36 MATERIALS SCIENCE↗
DOE OSTI2024

An integrated modeling framework with open architecture for phase field simulation of multi-component alloys

An integrated modeling framework (PanPhaseField) has been developed, which enables a direct and fast coupling between CALPHAD calculations and large-scale phase field simulations for multi-component alloys. Further, it adopts an open architecture allowing for integration of user-defined phase field models in a plug-and-play manner by taking full advantage of the user-friendly graphical interface of Pandat software. The developed modeling platform becomes an enabling tool that can be used to simulate the evolution of spatially varying microstructures of industrial complex alloys for various engineering applications.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Thermodynamic description of molten salt systems: KCl-LiCl-NaCl and KCl-LiCl-NdCl 3

The mixture of KCl and LiCl has been used as electrolyte in the electrorefining process to recover uranium from used nuclear fuels due to the low melting point. However, lanthanides and sodium in the reactor waste continuously dissolve into it and thus alter its thermodynamic properties. To understand the thermodynamic behavior evolution of the electrolyte with the accumulation of impurities, thermodynamic modeling for KCl-LiCl-NaCl and KCl-LiCl-NdCl 3 and four constituent binary systems in the entire composition space was performed using the CALPHAD (CALculation of PHAse Diagrams) approach. The ionic liquid was described by the two-sublattice model, where neutral species were introduced to consider short-range ordering (SRO) within the melt, whereas the solid solution was modeled based on the Compound Energy Formalism. Literature data on phase equilibria and thermochemical properties were critically evaluated and used during the optimization of thermodynamic parameters for KCl-LiCl-NaCl and KCl-LiCl-NdCl 3 and their subsystems. The calculated phase diagrams and mixing enthalpies are in good agreement with the experimental data. The thermodynamic modeling for the KCl-LiCl-NdCl 3 system was carried out for the first time. To fill the gap in experimental measurement, enthalpy of mixing for the KCl-LiCl-NdCl 3 melt was estimated using the surrounded-ion model. These data then served as critical inputs for thermodynamic optimization. Furthermore, the present study can provide insights into thermodynamic property evolution of the electrolyte and solubility limit of various impurities during the electrorefining process.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

CALPHAD Uncertainty Quantification and TDBX

CALPHAD uncertainty quantification (UQ) is the foundation of materials design with quantified confidence. We report a framework and software packages to enable CALPHAD UQ assessment and calculation using commercial CALPHAD software (Thermo-Calc). This Bayesian inference framework is coupled with a Markov chain Monte Carlo algorithm to establish uncertainty traces with a given thermodynamic database file (TDB) and corresponding experimental data points. This general framework is demonstrated with the Ni–Cr binary system. The algorithm is firstly validated on synthetic data with known ground truth. Then it is applied to real experimental data to generate posterior traces. We develop a file format named TDBX, which provides a single source of truth by combining the original TDB content and the traces for each assessed Gibbs energy parameter. CALPHAD UQ calculations are performed based on the TDBX file, from which uncertainties for phase boundaries, enthalpy curves, and solidification range are collected as examples of basic design parameters. This TDBX file with corresponding scripts are made open-source. Finally, the combination of CALPHAD UQ assessments and calculations connected by TDBX supports uncertainty-assisted modeling, enabling the integrated application of modern design with uncertainty methodologies to computational materials design.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Thermodynamics of Plutonium Monocarbide from Anharmonic and Relativistic Theory

Thermodynamics of plutonium monocarbide is studied from first-principles theory that includes relativistic electronic structure and anharmonic lattice vibrations. Density-functional theory (DFT) is expanded to include orbital-orbital coupling in addition to the relativistic spin-orbit interaction for the electronic structure and it is combined with anharmonic, temperature dependent, lattice dynamics derived from the self-consistent ab initio lattice dynamics (SCAILD) method. The obtained thermodynamics are compared to results from simpler quasi-harmonic theory and experimental data. Formation enthalpy, specific heat, and Gibbs energy calculated from the anharmonic model are validated by direct comparison with a calculation of phase diagram (CALPHAD) assessment of PuC and sub-stochiometric PuC0.896. Overall, the theory reproduces CALPHAD results and measured data for PuC rather well, but the comparison is hampered by the sub-stoichiometric nature of plutonium monocarbide. It was found that a bare theoretical approach that ignores spin-orbit and orbital-orbital coupling (orbital polarization) of the plutonium 5f electrons promotes too soft phonons and Gibbs energies that are incompatible with that of the CALPHAD assessment of the experimental data. The investigation of PuC suggests that the electronic structure is accurately described by plutonium 5f electrons as “band like” and delocalized, but correlate through spin polarization, orbital polarization, and spin-orbit coupling, in analogy to previous findings for plutonium metal.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗