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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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DOE OSTI2021

Theoretical predictive screening of noble-metal-containing M 3 AuC 2 (M = Ti, V, and Cr) MAX phases

Using first-principles density functional theory (DFT), the ground state physical properties of the newly synthesized noble-metal-containing Ti 3 AuC 2 MAX phase have been investigated. The effect of transition-element Ti replacement (V and Cr) on physical properties, including structural, elastic, electronic, thermal, and optical are presented. The optimized lattice parameters of Ti 3 AuC 2 MAX phase are in good agreement with the experimental values and decrease when we replace Ti with V and then Cr. The magnetic properties of M 3 AuC 2 are predicted by using generalized gradient approximation (GGA) incorporating onsite electron correlation parameter (Hubbard U parameter) within DFT. Out of three studied MAX phases, V 3 AuC 2 and Cr 3 AuC 2 phases exhibit magnetism. The thermodynamical stability of M 3 AuC 2 (M = Ti, V, and Cr) phases is discussed by the formation enthalpy with respect to their most competing phases. The electronic structure reveals that these phases have metallic nature and are electrically anisotropic. The M-element replacement has an effect on the bonding properties of M 3 AuC 2 . The bonding between M-C is in the order of Cr 3 AuC 2 > V 3 AuC 2 > Ti 3 AuC 2 according to their peak positions and heights of density of states in the occupied site, which is also confirmed from the charge density distribution. The elastic properties indicate that M 3 AuC 2 phases are ductile, machinable, less stiff, and better resistant to thermal shock and are elastically anisotropic. To understand the properties of M 3 AuC 2 for the extreme environment, we employed a quasi-harmonic Debye model at the pressure and temperature range of 0–50 GPa and 0–1600 K, respectively. Additionally, we evaluated thermal conductivity and melting temperature to further understand the potential of these MAX phases in coating applications at elevated temperature. In relation to optical properties, these MAX phases have a reasonable absorption coefficient in the visible as well as in the ultra-violet regions. The reflectivity of M 3 AuC 2 phases is polarization dependent and of 45 % against the visible region, which reveals its potential as coating material to minimize solar heating.

312 MAX phases↗
DOE OSTI2022

First principles prediction of the Al-Li phase diagram including configurational and vibrational entropic contributions

The whole Al-Li phase diagram is predicted from first principles calculations and statistical mechanics including the effect of configurational and vibrational entropy. The formation enthalpy of different configurations at different temperatures was accurately predicted by means of cluster expansions that were fitted from first principles calculations. The vibrational entropic contribution of each configuration was determined from the bond length vs. bond stiffness relationships for each type of bond and the Gibbs free energy of the different phases was obtained as a function of temperature from Monte Carlo simulations. The predicted phase diagram was in excellent agreement with the currently accepted experimental one in terms of the stable (AlLi, Al 2 Li 3 , AlLi 2 , Al 4 Li 9 ) and metastable (Al 3 Li) phases, of the phase boundaries between them and of the maximum stability temperature of line compounds. In addition, it provided accurate information about the gap between Al 3 Li and AlLi solvus lines. Finally, the influence of the vibrational entropy on the correct prediction of the phase diagram is discussed. Overall, the methodology shows that accurate phase diagrams of alloys of technological interest can be predicted from first principles calculations.

36 MATERIALS SCIENCE↗
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 OSTI2024

Machine learning in materials research: Developments over the last decade and challenges for the future

The number of studies that apply machine learning (ML) to materials science has been growing at a rate of approximately 1.67 times per year over the past decade. In this review, I examine this growth in various contexts. First, I present an analysis of the most commonly used tools (software, databases, materials science methods, and ML methods) used within papers that apply ML to materials science. The analysis demonstrates that despite the growth of deep learning techniques, the use of classical machine learning is still dominant as a whole. It also demonstrates how new research can effectively build upon past research, particular in the domain of ML models trained on density functional theory calculation data. Next, I present the progression of best scores as a function of time on the matbench materials science benchmark for formation enthalpy prediction. In particular, a dramatic improvement of 7 times reduction in error is obtained when progressing from feature-based methods that use conventional ML (random forest, support vector regression, etc.) to the use of graph neural network techniques. Finally, I provide views on future challenges and opportunities, focusing on data size and complexity, extrapolation, interpretation, access, and relevance.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A machine learning approach to quantify degradation of nuclear fuels and the effects of fission products

Nuclear fuel performance is critically dependent on understanding the evolution of fuel properties under operational conditions, a complex challenge driven by chemical changes and substantial radiation damage during fission. Traditionally, property evolution has been determined via empirical data collected following irradiation. However, these empirical correlations are limited in their applicability beyond the specific conditions in which they were obtained. This study explores a novel approach to address this challenge by applying materials informatics to develop a machine learning random forest (ML-RF) model that captures the effects of fission products on fuel compounds. The model predicts formation enthalpy (ΔH f ) by leveraging extensive quantum materials property data and correlating it with material descriptors such as composition, atomic and site features, and crystal lattice properties. This ML-RF model enables rapid interpolation across the compositional and structural spaces covered by the training data, thus supporting high-throughput screening and energetic ranking of candidate phases. The model demonstrates the ability to predict ΔH f with a mean absolute error (MAE) of approximately 0.1 to 0.2 eV/atom across a wide range of compounds, including key nuclear fuel systems (U-O, U-N, U-C, U-Si, and U-Mo). For example, it was used to assess shifts in stoichiometry for UO 2 (O/M) and UN (N/M) fuels, revealing their distinct tendencies in chemical potential variation and enabling preliminary convex hull analyses. Furthermore, the model provides insights into how individual fission products affect fuel properties. Results indicate that larger fission products (e.g., Nd, Pu, Ce) have a more pronounced impact on UO 2 , while lighter ones (e.g., Zr) strongly influence UN. Here, the model developed in this work can be used to support the Accelerated Fuel Qualification approach by facilitating preliminary evaluations prior to extensive materials modeling and experimentation. To this end, the trained model has been made available to the fuel community to support ongoing fuel development efforts.

Accelerated fuel qualification↗
DOE OSTI2025

Ab-initio informed cluster dynamics simulation of self- and Xe diffusivity in uranium mononitride under irradiation

Uranium mononitride (UN) is one of the ceramic nuclear fuel alternatives to oxide fuels considered for light water reactor and advanced reactor designs, as it presents significant advantages such as high uranium density (better economics) and high thermal conductivity and melting point (increased safety). Self- and fission gas diffusivities need to be better understood, given that they influence key fuel performance phenomena like swelling and fission gas release. Recently, radiation enhanced diffusivity was investigated in UN by means of cluster dynamics simulations relying on empirical potential-based parameterizations, the reliability of which highly depends on the interatomic potential accuracy. Here, in this work, we refine this approach by determining, using ab-initio calculations, the properties of defect clusters containing vacancies, self-interstitials and Xe impurities. We also consider larger clusters than previous studies. The obtained dataset (formation enthalpies, entropies, and migration barriers) is used to parameterize a cluster dynamics model of mobile clusters, and to calculate the defect cluster concentrations under irradiation. This gives us access to the radiation enhanced self- and fission gas diffusivities. Although the resulting diffusivities are close to the values reported in the literature, we find important qualitative differences in the diffusion mechanisms. Capturing the correct mechanisms is crucial to properly describe the chemistry and fission rate dependence of the model.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

A new high voltage alluaudite sodium battery insertion material

Large-scale stationary storage forms a key sector that can be economically served by sodium-ion batteries. In realizing practical sodium-ion batteries, discovery and development of novel cathodes is essential. In this spirit, alluaudite-type Na 2 Fe 2 (SO 4 ) 3 was reported in 2014 to have the highest Fe 3+ /Fe 2+ redox potential (~3.8 V vs. Na). This finding led to reports on various PO4 3– and SO4 2– based alluaudite compounds exhibiting high energy densities. In 2017, MoO 4 2– based alluaudite, Na 2.67 Mn 1.67 (MoO 4 ) 3 , was found as a 3.45 V cathode material. Exploring molybdenum chemistry further, this work reports alluaudite type Na 3.36 Co 1.32 (MoO 4 ) 3 (NCMo) as a novel versatile electroactive cathode for Li-ion and Na-ion batteries. It was synthesized by a wet solution-combustion route with a restricted annealing duration of 1 min at 600 °C. Calorimetric study revealed the formation enthalpy from component oxides (ΔH° f,ox = –575.49 ± 7.75 kJ/mol) to be highly exothermic. Unlike the sulfate class of alluaudites, this material is highly stable in air and moisture (ΔH ds = 537.42 ± 0.78 kJ/mol). Having an ionic conductivity of 6.065 × 10 –8 S/cm (at 50 °C), it offers a pseudo two-dimensional Na + migration pathway. Without any material optimization, NCMo was found to work as a high-voltage insertion cathode (ca. 4.0 V vs. Na/Na + and 4.1 V vs. Li/Li + ) in sync with theoretically predicted potential of 3.98 V (vs. Na/Na + ). Ex-situ X-ray diffraction and photoelectron spectroscopy studies revealed the occurrence of solid-solution redox mechanism solely involving Co 3+ /Co 2+ redox centre. Finally, it benchmarks Na 3.36 Co 1.32 (MoO 4 ) 3 as a novel electrochemically active Mo-based alluaudite-type polyanionic cathode insertion material.

25 ENERGY STORAGE↗
DOE OSTI2020

Thermodynamics of cesium lead halide (CsPbX 3 , x= I, Br, Cl) perovskites

Inorganic cesium lead halide (CsPbX 3 , X = I, Br, Cl) perovskites have sparked broad interest for photovoltaic applications, which, however, may be plagued by chemical and thermodynamic instabilities. This work reports calorimetric studies on the formation enthalpies of CsPbX 3 perovskites, confirming decreasing thermodynamic stability in the order CsPbCl 3 , CsPbBr 3 and CsPbI 3 . Cesium lead iodide (CsPbI 3 ), one of the most studied inorganic lead halide perovskites, is shown to be only barely stable from a thermodynamic perspective. This order of increasing instability is consistent with the variation of Goldschmidt tolerance factor. The perovskites are high temperature entropy-stabilized phases with respect to low temperature polymorphs, with the thermodynamics of phase transitions reflecting the above thermodynamic trends.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Computationally Predicted High-Throughput Free-Energy Phase Diagrams for the Discovery of Solid-State Hydrogen Storage Reactions

The design of multinary solid-state material systems that undergo reversible phase changes via changes in temperature and pressure provides a potential means of safely storing hydrogen. However, fully mapping the stabilities of known or newly targeted compounds relative to competing phases at reaction conditions has previously required many stringent experiments or computationally demanding calculations of each compound’s change in Gibbs energy with respect to temperature, G(T). Here, we have extended the approach of constructing chemical potential phase diagrams based on ΔG f (T) to enable the analysis of phase stability at non-zero temperatures. We first performed density functional theory calculations to compute the formation enthalpies of binary, ternary, and quaternary compounds within several compositional spaces of current interest for solid-state hydrogen storage. Temperature effects on solid compound stability were then accounted for using our recently introduced machine learned descriptor for the temperature-dependent contribution G δ (T) to the Gibbs energy G(T). From these Gibbs energies, we evaluated each compound’s stability relative to competing compounds over a wide range of conditions and show using chemical potential and composition phase diagrams that the predicted stable phases and H2 release reactions are consistent with experimental observations. This demonstrates that our approach rapidly computes the thermochemistry of hydrogen release reactions for compounds at sufficiently high accuracy relative to experiment to provide a powerful framework for analyzing hydrogen storage materials. This framework based on G(T) enables the accelerated discovery of active materials for a variety of technologies that rely on solid-state reactions involving these materials.

08 HYDROGEN↗
DOE OSTI2022

A theoretical investigation of the effect of Ga alloying on thermodynamic stability, electronic-structure, and oxidation resistance of Ti 2 AlC MAX phase

We present a systematic investigation of thermodynamic stability, phase-reaction, and chemical activity of Al containing disordered Ti 2 (Al-Ga)C MAX phases using machine-learning driven high-throughput framework to understand the oxidation resistance behavior with increasing temperature and exposure to static oxygen. The A-site (at Al) disordering of Ti 2 AlC with Ga shows significant change in the chemical activity of Al with increasing temperature and exposure to static oxygen, which is expected to enable surface segregation of Al, thereby, the formation of Al 2 O 3 and improved oxidation resistance. We performed in-depth convex hull analysis of ternary Ti-Al-C, Ti-Ga-C, and Ti-Al-Ga-C based MAX phase, and provide detailed contribution arising from electronic, chemical and vibrational entropies. The thermodynamic analysis shows change in the Gibbs formation enthalpy (ΔG form ) at higher temperatures, which implies an interplay of temperature-dependent enthalpy and entropic contributions in oxidation resistance Ga doped Ti 2 AlC MAX phases. A detailed electronic structure and chemical bonding analysis using crystal orbital Hamilton population method reveal the origin of change in phases stability and in oxidation resistance in disorder Ti2(Al 1-x Ga x )C MAX phases. Our electronic structure analysis correlate well with the change in oxidation resistance of Ga doped MAX phases. We believe our study provides a useful guideline to understand to role of alloying on electronic, thermodynamic, and oxidation related mechanisms of bulk MAX phases, which can work as a precursor to understand oxidation behavior of twodimensional MAX phases, i.e., MXenes (transition metal carbides, carbonitrides and nitrides).

36 MATERIALS SCIENCE↗
DOE OSTI2023

Defect graph neural networks for materials discovery in high-temperature clean-energy applications

We present a graph neural network approach that fully automates the prediction of defect formation enthalpies for any crystallographic site from the ideal crystal structure, without the need to create defected atomic structure models as input. Here we used density functional theory reference data for vacancy defects in oxides, to train a defect graph neural network (dGNN) model that replaces the density functional theory supercell relaxations otherwise required for each symmetrically unique crystal site. Interfaced with thermodynamic calculations of reduction entropies and associated free energies, the dGNN model is applied to the screening of oxides in the Materials Project database, connecting the zero-kelvin defect enthalpies to high-temperature process conditions relevant for solar thermochemical hydrogen production and other energy applications. The dGNN approach is applicable to arbitrary structures with an accuracy limited principally by the amount and diversity of the training data, and it is generalizable to other defect types and advanced graph convolution architectures. In conclusion, it will help to tackle future materials discovery problems in clean energy and beyond.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Crystal structure of silver pentazolates AgN 5 and AgN 6

Silver pentazolate, a high energy density compound containing the cyclo-N 5 – anion, has recently been synthesized under ambient conditions. However, due to high sensitivity to irradiation, its crystal structure has not been determined. In this work, silver–nitrogen crystalline compounds under ambient conditions and at high pressures, up to 100 GPa, are predicted and characterized by performing first-principles evolutionary crystal structure searching with variable stoichiometry. It is found that newly discovered AgN 5 and AgN 6 are the only thermodynamically stable silver–nitrogen compounds at pressures between 42 and 80 GPa. In contrast to AgN 5 , the pentazolate AgN 6 compound contains N 2 diatomic molecules in addition to cyclo-N 5 – . These AgN 5 and AgN 6 crystals are metastable under ambient conditions with positive formation enthalpies of 54.95 kJ mol –1 and 46.24 kJ mol –1 , respectively. Here, the underlying cause of the stability of cyclo-N 5 – silver pentazolates is the enhanced aromaticity enabled by the charge transfer from silver atoms to nitrogen rings. To aid in the experimental identification of these materials, calculated Raman spectra are reported at ambient pressure: the frequencies of N 5 – vibrational modes of AgN 5 are in good agreement with those measured in the experiment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Machine learning for design principles for single atom catalysts towards electrochemical reactions

Machine learning (ML) integrated density functional theory (DFT) calculations have recently been used to accelerate the design and discovery of heterogeneous catalysts such as single atom catalysts (SACs) through the establishment of deep structure–activity relationships. Here, this review provides recent progress in the ML-aided rational design of heterogeneous catalysts with the focus on SACs in terms of structure–activity relationships, feature importance analysis, high-throughput screening, stability, and metal–support interactions for electrochemistry. Support vector machine (SVM), random forest regression (RFR), and deep neural networks (DNN) along with atomic properties are mainly used for the design of SACs. The ML results have shown that the number of electrons in the d orbital, oxide formation enthalpy, ionization energy, Bader charge, d-band center, and enthalpy of vaporization are mainly the most important parameters for the defining of the structure–activity relationships for electrochemistry. However, the black-box nature of ML techniques occasionally makes a physical interpretation of descriptors, such as the Bader charge, d-band center, and enthalpy of vaporization, non-trivial. At the current stage, ML application is limited by the lack of a large and high-quality database. Future prospects for the development of a large database and a generalized ML algorithm for SAC design are discussed to give insights for further studies in this field.

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

Thermodynamic properties and enhancement of diamagnetism in nitrogen doped lutetium hydride synthesized at high pressure

Nitrogen doped lutetium hydride has drawn global attention in the pursuit of room-temperature superconductivity near ambient pressure and temperature. However, variable synthesis techniques and uncertainty surrounding nitrogen concentration have contributed to extensive debate within the scientific community about this material and its properties. We used a solid-state approach to synthesize nitrogen doped lutetium hydride at high pressure and temperature (HPT) and analyzed the residual starting materials to determine its nitrogen content. High temperature oxide melt solution calorimetry determined the formation enthalpy of LuH 1.96 N 0.02 (LHN) from LuH 2 and LuN to be −28.4 ± 11.4 kJ/mol. Magnetic measurements indicated diamagnetism which increased with nitrogen content. Ambient pressure conductivity measurements observed metallic behavior from 5 to 350 K, and the constant and parabolic magnetoresistance changed with increasing temperature. High pressure conductivity measurements revealed that LHN does not exhibit superconductivity up to 26.6 GPa. We compressed LHN in a diamond anvil cell to 13.7 GPa and measured the Raman signal at each step, with no evidence of any phase transition. Despite the absence of superconductivity, a color change from blue to purple to red was observed with increasing pressure. Thus, our findings confirm the thermodynamic stability of LHN, do not support superconductivity, and provide insights into the origins of its diamagnetism.

Science & Technology - Other Topics↗
DOE OSTI2026

The origin of metallic conductivity in Pt 3 O 4 : a first principles study

The platinum oxide Pt 3 O 4 exhibits metallic conductivity even though it contains square-planar PtO4 units, which in related oxides such as PtO are usually associated with insulating behavior. To identify the electronic origin of this anomalous metallicity, we performed a comprehensive first-principles study using the PBE and r 2 SCAN functionals together with Hubbard U corrections and spin-orbit coupling (SOC). Structural benchmarks show that r 2 SCAN with SOC and a moderate U value (<4 eV) reproduces the experimental lattice constants and formation enthalpy, whereas larger U values (~8 eV) destabilize the cubic structure. Across all functionals and U values considered in this work, Pt 3 O 4 remains metallic. Analyses of the projected density of states, band structures, charge-density isosurfaces, and bonding characteristics demonstrate that the dominant contribution to the metallic character originates from delocalized Pt–O–Pt hybridized antibonding states at the Fermi level. Direct Pt–Pt interactions are present but contribute less strongly to the conductivity. Bader charge analysis reveals only weak Pt charge disproportionation, consistent with mixed Pt II /Pt III character, and a small charge-transfer energy that prevents localization of the Pt 5d electrons even at elevated U. In contrast, PtO develops a Mott or charge-transfer gap under modest U despite having the same PtO 4 coordination environment. These findings demonstrate that persistent Pt–O–Pt covalency is the primary driver of metallicity in Pt 3 O 4 and support the view that this phase can remain conductive under oxygen reduction and oxygen evolution reaction conditions in fuel cell and electrolyzer environments.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Probing configurational disorder in ZnGeN 2 using cluster-based Monte Carlo

ZnGeN 2 is sought as a semiconductor with comparable lattice constant to GaN and tunable band gap for integration in optoelectronic devices. Configurational disorder on the cation sublattice of ZnGeN 2 can strongly modify the electronic structure compared to the ordered material, and both ordered and disordered forms of ZnGeN 2 are candidates for light-emitting diodes and other emitters. The nonisovalent character of the disordered species (Zn 2+ and Ge 4+ ) subjects the cation ordering to strong short-range order effects. To model these effects, we use Monte Carlo (MC) simulations utilizing a cluster expansion to approximate formation enthalpy. Representative disordered configurations in 1024-atom supercells are relaxed in density functional theory calculations. From the MC structures, we extract a short-range order parameter (the N-cation coordination motif), and two long-range order parameters (Bragg-Williams and stretching parameters), and examine their correlations. We perform a thermodynamic integration to determine the mixing entropy and free energy. ZnGeN 2 exhibits a first-order phase transition with pronounced discontinuities in enthalpy and entropy, as well as in the structural order parameters. We discuss the relationship between the effective temperature used in the MC simulation and the growth temperatures in experiment in relation to the crossover from the nonequilibrium to the equilibrium growth regime. This work expands on current models of site disorder in ZnGeN 2 and provides atomic structure models with a systematic variation of the degree of cation disorder.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Wide-ranging predictions of new stable compounds powered by recommendation engines

The computational search for new stable inorganic compounds is faster than ever, thanks to high-throughput density functional theory (DFT). However, stable compound searches remain highly expensive because of the enormous search space and the cost of DFT calculations. To aid these searches, recommendation engines have been developed. We conduct a systematic comparison of the performance of previously developed recommendation engines, specifically ones based on elemental substitution, data mining, and neural network prediction of formation enthalpy. After identifying ways to improve the recommendation engines, we find the neural network to be superior at recommending stable Heusler compounds. Armed with improved recommendation engines, we identify tens of thousands of compounds that are stable at zero temperature and pressure, now available in the Open Quantum Materials Database. We summarize this diverse pool of compounds, including the elusive mixed anion compounds, and two of their many applications: thermoelectricity and solar thermochemical fuel production.

Science & Technology - Other Topics↗