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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1996

Electronic Characterization of Defects in Narrow Gap Semiconductors-Comparison of Electronic Energy Levels and Formation Energies in Mercury Cadmium Telluride, Mercury Zinc Telluride, and Mercury Zinc Selenide

We have used a Green's function technique to calculate the energy levels and formation energy of deep defects in the narrow gap semiconductors mercury cadmium telluride (MCT), mercury zinc telluride (MZT) and mercury zinc selenide (MZS). The formation energy is calculated from the difference between the total energy with an impurity cluster and the total energy for the perfect crystal. Substitutional (including antisite), interstitial (self and foreign), and vacancy deep defects are considered. Relaxation effects are calculated (with molecular dynamics). By use of a pseudopotential, we generalize the ideal vacancy model so as to be able to consider relaxation for vacancies. Different charge states are considered and the charged state energy shift (as computed by a modified Haldane-Anderson model) can be twice that due to relaxation. Different charged states for vacancies were not calculated to have much effect on the formation energy. For all cases we find deep defects in the energy gap only for cation site s-like orbitals or anion site p-like orbitals, and for the substitutional case only the latter are appreciably effected by relaxation. For most cases for MCT, MZT, MZS, we consider x (the concentration of Cd or Zn) in the range appropriate for a band gap of 0.1 eV. For defect energy levels, the absolute accuracy of our results is limited, but the precision is good, and hence chemical trends are accurately predicted. For the same reason, defect formation energies are more accurately predicted than energy level position. We attempt, in Appendix B, to calculate vacancy formation energies using relatively simple chemical bonding ideas due to Harrison. However, these results are only marginally accurate for estimating vacancy binding energies. Appendix C lists all written reports and publications produced for the grant. We include abstracts and a complete paper that summarizes our work which is not yet available.

Patterson, James D.↗
DOE OSTI2026

Two-stage formation-energy correction (NbZr, TaZr, VZr)

This bundle contains the scripts, the raw and corrected per-structure data, and the manuscript plots for the NbZr / TaZr / VZr BCC binary formation energies and the associated RMSDs. Why a two-stage correction is necessary: The "raw" formation energy of every relaxed VASP configuration is computed in the usual way, FE_raw(c) = E_alloy(c) - sum_i x_i * E_pure_i , where E_pure_i are the per-atom total energies of the pure-element reference structures (Nb, Ta, V, Zr in the same BCC supercell, with identical INCAR / KPOINTS / PAW choices). With perfectly consistent reference runs the raw FE should vanish at the two pure-element endpoints (x = 0 and x = 1) by construction. In practice this does not hold for two reasons that are present in our dataset: 1. Reference-energy inconsistency (composition-dependent bias). Even with identical input parameters, the pure-element runs (stored in `corrected_DFT_pure_element_runs/`) differ slightly from the values that would be implied by the alloy runs at near-pure compositions (a few meV/atom). This bias is approximately linear in concentration, because the residual error in E_pure_Nb (or E_pure_Ta / E_pure_V) propagates into FE_raw(c) as (1 - x) * dE_pure_1, and the corresponding error in E_pure_Zr propagates as x * dE_pure_2. Left uncorrected, this produces a non-physical "tilt" of FE_raw(x) and shifts the entire FE-vs-x cloud away from zero at the endpoints. 2. Endpoint anchoring against the audited true endpoints. The strict endpoint values (FE_x0_meVatom, FE_x1_meVatom in `corrected_fe_strict_endpoints_20260518/strict_endpoint_check_20260518.csv`) were re-derived from an independent cross-check of the pure-element runs. After stage 1 removes the linear bias, the near-pure compositions in the alloy dataset still extrapolate to values that differ slightly from these audited endpoints — because stage 1 is fit from a few near-end alloy bins, not from the audited pure-element references themselves. The README.txt file discusses how these issues are addressed by the two-stage correction, and describes folder layout, pipeline summary, and how to re-run.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Formation energy puzzle in intermetallic alloys: Random phase approximation fails to predict accurate formation energies

We performed density functional calculations to estimate the formation energies of intermetallic alloys. We used two semilocal approximations, the generalized gradient approximation (GGA) by Perdew-Burke- Ernzerhof (PBE) and the strongly constrained and appropriately normed (SCAN) meta-GGA. In addition, we utilized two nonlocal DFT functionals, the hybrid HSE06, and the state-of-the-art random phase approximation (RPA). The nonlocal functionals such as HSE06 and RPA yield accurate formation energies of binary alloys with completely-filled d-band metals, where semilocal functionals underperform. The accuracy at the nonlocal functionals is greatly reduced when a partially-filled d-band metal is present in an alloy, while PBE-GGA outperforms in these cases. We show that the accurate prediction of formation energies by any DFT method depends on its ability to predict the accurate electronic properties, e.g., valence d-band contribution to the density of states (DOS). The SCAN meta-GGA often corrects the PBE-DOS, however, it does not provide accurate formation energies compared to PBE. This is assumed to be due to the lack of proper error cancellation that should be expected due to the similar bulk nature of both alloys and their constituents, which may improve with the modification of meta-GGA ingredients. RPA yields too negative formation energies of alloys with partially-filled d-band metals. RPA results can be corrected by restoring the exchange-correlation kernel, thereby improving the short-range electron-electron correlation in metallic densities.

36 MATERIALS SCIENCE↗
NASA NTRS1995

Electronic characterization of defects in narrow gap semiconductors: Comparison of electronic energy levels and formation energies in mercury cadmium telluride, mercury zinc telluride, and mercury zinc selenide

The project has evolved to that of using Green's functions to predict properties of deep defects in narrow gap materials. Deep defects are now defined as originating from short range potentials and are often located near the middle of the energy gap. They are important because they affect the lifetime of charge carriers and hence the switching time of transistors. We are now moving into the arena of predicting formation energies of deep defects. This will also allow us to make predictions about the relative concentrations of the defects that could be expected at a given temperature. The narrow gap materials mercury cadmium telluride (MCT), mercury zinc telluride (MZT), and mercury zinc selenide (MZS) are of interest to NASA because they have commercial value for infrared detecting materials, and because there is a good possibility that they can be grown better in a microgravity environment. The uniform growth of these crystals on earth is difficult because of convection (caused by solute depletion just ahead of the growing interface, and also due to thermal gradients). In general it is very difficult to grow crystals with both radial and axial homogeneity.

Patterson, James D.↗
DOE OSTI2023

Assessment of exchange-correlation functionals on oxygen vacancy formation energies of metal oxides

Oxygen vacancies are a common defect in oxides and play vital roles in many technological applications such as oxygen separation, catalytic reactors, solid oxide fuel cells, and solar thermochemical water splitting. The oxygen vacancy formation energy is directly related to the oxide reduction enthalpy and the vacancy concentration and, therefore, is a key quantity underlying these applications. The complexity of measuring oxygen vacancy formation energies experimentally suggests the utility of calculations based on density functional theory (DFT). However, the calculated results are strongly dependent on the exchange-correlation functionals and other parameters such as the Hubbard U . In this work, we compare the performance of the meta-GGA strongly constrained and appropriately normed (SCAN) functional and the commonly used semilocal generalized gradient approximation (GGA) functionals with experimental values of structural parameters, band gaps, magnetic structures, and oxygen vacancy formation energies for six representative oxides, i.e., CaMnO 3 , SrMnO 3 , LaMnO 3 , YMnO 3 , LaFeO 3 , and CeO 2 . Further, our results show that SCAN usually has better agreement with the experimental lattice constants and band gaps and larger magnetic moments than the commonly used GGA functionals. Although SCAN overestimates the oxygen vacancy formation energies of transition metal oxides and therefore requires unusually large Hubbard U values to reproduce the experimental reduction enthalpies, it does predict the correct oxygen vacancy formation energy of CeO 2 , which challenges the commonly used GGA and hybrid functionals. Our results underscore the challenges that exist in describing these complex oxides by DFT and may shed light on developing more accurate exchange-correlation functionals for oxygen vacancy formation energy calculations.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Persistence of negative vacancy and self-interstitial formation energies in atomistic models of amorphous silicon

We use classical potential atomistic modeling to demonstrate that negative vacancy and selfinterstitial formation energies are inherently present in amorphous silicon (a-Si). Our conclusions are based on comprehensive calculations of vacancy and self-interstitial formation energies in nine different models, ranging in size from 1000 to 2744 atoms and formed by quenching liquid Si at rates between 10 10 and 10 12 K/s. We account for a range of possible reference energies corresponding to different choices of reservoirs for atoms inserted into or removed from our models. Moreover, we show that successive addition of either vacancies or self-interstitials does not deplete the number of negative formation energy sites. Instead, it further lowers defect formation energies. Furthermore, these findings lead us to conclude that a-Si is not a structure at or near a local enthalpy minimum nor can it be brought to a local enthalpy minimum through the introduction of constitutional defects.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Assessing the accuracy of compound formation energies with quantum Monte Carlo

Accurately predicting the formation energy of a compound, which describes its thermodynamic stability, is a key challenge in materials physics. Here, we employ a many-body quantum Monte Carlo (QMC) with single-reference trial functions to compute the formation energy of two electronically disparate compounds, the intermetallic VPt2 and the semiconductor CuI, for which standard density functional theory (DFT) predictions using both the Perdew-Burke-Ernzerhof (PBE) and the strongly constrained and appropriately normed (SCAN) density functional approximations deviate markedly from available experimental values. For VPt2, we find an agreement between QMC, SCAN, and PBE0 estimates, which therefore remain in disagreement with the much less exothermic experimental value. For CuI, the QMC result agrees with neither SCAN nor PBE pointing towards DFT exchange-correlation biases, likely related to the localized Cu 3d electrons. Compared to the behavior of some density functional approximations within DFT, spin-averaged QMC exhibits a smaller but still appreciable deviation when compared to experiment. The QMC result is slightly improved by incorporating spin-orbit corrections for CuI and solid I 2 , so that experiment and theory are brought into imperfect but reasonable agreement within about 120 meV/atom.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

A critical examination of compound stability predictions from machine-learned formation energies

Machine learning has emerged as a novel tool for the efficient prediction of material properties, and claims have been made that machine-learned models for the formation energy of compounds can approach the accuracy of Density Functional Theory (DFT). The models tested in this work include five recently published compositional models, a baseline model using stoichiometry alone, and a structural model. By testing seven machine learning models for formation energy on stability predictions using the Materials Project database of DFT calculations for 85,014 unique chemical compositions, we show that while formation energies can indeed be predicted well, all compositional models perform poorly on predicting the stability of compounds, making them considerably less useful than DFT for the discovery and design of new solids. Most critically, in sparse chemical spaces where few stoichiometries have stable compounds, only the structural model is capable of efficiently detecting which materials are stable. The nonincremental improvement of structural models compared with compositional models is noteworthy and encourages the use of structural models for materials discovery, with the constraint that for any new composition, the ground-state structure is not known a priori. This work demonstrates that accurate predictions of formation energy do not imply accurate predictions of stability, emphasizing the importance of assessing model performance on stability predictions, for which we provide a set of publicly available tests.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Transferring predictions of formation energy across lattices of increasing size*

In this study, we show the transferability of graph convolutional neural network (GCNN) predictions of the formation energy of the nickel-platinum solid solution alloy across atomic structures of increasing sizes. The original dataset was generated with the large-scale atomic/molecular massively parallel simulator using the second nearest-neighbor modified embedded-atom method empirical interatomic potential. Geometry optimization was performed on the initially randomly generated face centered cubic crystal structures and the formation energy has been calculated at each step of the geometry optimization, with configurations spanning the whole compositional range. Using data from various steps of the geometry optimization, we first trained our open-source, scalable implementation of GCNN called HydraGNN on a lattice of 256 atoms, which accounts well for the short-range interactions. Using this data, we predicted the formation energy for lattices of 864 atoms and 2048 atoms, which resulted in lower-than-expected accuracy due to the long-range interactions present in these larger lattices. We accounted for the long-range interactions by including a small amount of training data representative for those two larger sizes, whereupon the predictions of HydraGNN scaled linearly with the size of the lattice. Therefore, our strategy ensured scalability while reducing significantly the computational cost of training on larger lattice sizes.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Revealing the Formation Energy–Exfoliation Energy–Structure Correlation of MAB Phases Using Machine Learning and DFT

MAB phases are popular as high-temperature materials with high damage tolerance and excellent electrical conductivity that are used to exfoliate two-dimensional (2D) transition metal borides (MBenes); a promising material for developing next- generation nanodevices. In this report, we explore the correlation between the formation energy, exfoliation energy and structural factors of MAB phases using DFT and machine learning. In order to do so, we developed three different machine learning models based on the support vector machine, deep neural network, and random forest regressor to study the stability of the MAB phases by calculating their formation energies. Furthermore, our support vector machine and deep neural network models are capable of predicting the formation energies with mean absolute errors less than 0.1 eV/atom. We demonstrated that the stability of a MAB phase for a given transition metal decreases when the A element changes from Al to Tl. The density functional theory (DFT) re- vealed that M-A and M-B bond strength strongly correlates with the stability of MAB phases. In addition, the exfoliation possibility of 2D MBenes becomes higher when the A element changes from Al to Tl due to weakening of M-A and M-B bonds.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1994

Formation Energy of Disordered Alloys form the Energetics of Ordered Compounds

The cluster expansion technique is used in combination with first-principles calculations of the total energy of ordered compounds to study the energetics of the disordered phase for a number of alloy systems. The effect of short-range order, as seen in the energy differences between an alloy with the configuration corresponding to finite temperature and the perfectly random one is studied. The role of relaxation due to large size mismatch is discussed in terms of an effective cluster volume approximation. Very good agreement with measured energies of formation is obtained when short-range order and relaxation effects are taken into account.

Amador, Carlos↗
DOE OSTI2026

Machine Learning Vacancy Formation Energy in Nickel-Based Superalloys

Thermal vacancies play a critical role in high-temperature Ni-based superalloys and influence various properties such as creep resistance, oxidation, etc. This study systematically investigates the impact of commonly used transition metals (Cr, Co, Fe), refractory metals (Nb, Ta, Mo, W) and other elements (Al, Cu, Ti, Mn) on the thermodynamic stability of 36 binary, 20 ternary, 11 quaternary, 9 quinary, and 3 senary FCC Ni-based alloys covering various elemental combinations. Density functional theory-based studies on Ni-X binary alloys show that higher concentrations of Cr, Nb, Ta, Al, and Ti introduce significant lattice distortions and broaden the distribution of vacancy formation energies (standard deviation up to 0.15 eV). These elements partially donate electrons, reducing their self-consistent chemical potentials relative to single-element reference values and lowering vacancy formation energies, while Co, Fe, Mo, and W show lower charge localization. These trends extend from 3-6 element alloys, where Cr, Nb, and Ta-rich compositions have low-energy states (~0.5 eV) that increase vacancy concentrations. Finally, graph neural network models are developed to screen over 5000 virtual alloys. Eleven leading compositions are identified with mean vacancy formation energy higher than 1.75 eV and vacancy concentration ~2 orders of magnitude lower than pure Ni at 1000 K. These results provide valuable guidelines to achieve controlled defect engineering in structural alloys.

DFT↗
NASA NTRS2002

Solid Hydrogen Experiments for Atomic Propellants: Particle Formation Energy and Imaging Analyses

This paper presents particle formation energy balances and detailed analyses of the images from experiments that were conducted on the formation of solid hydrogen particles in liquid helium during the Phase II testing in 2001. Solid particles of hydrogen were frozen in liquid helium and observed with a video camera. The solid hydrogen particle sizes and the total mass of hydrogen particles were estimated. The particle formation efficiency is also estimated. Particle sizes from the Phase I testing in 1999 and the Phase II testing in 2001 were similar. Though the 2001 testing created similar particles sizes, many new particle formation phenomena were observed. These experiment image analyses are one of the first steps toward visually characterizing these particles and it allows designers to understand what issues must be addressed in atomic propellant feed system designs for future aerospace vehicles.

Palaszewski, Bryan↗
DOE OSTI2025

Machine Learning Vacancy Formation Energy in Nickel-Based Superalloys

Creep performance plays a key role in nickel-based superalloys for high temeprature applications. Creep behavior depends on many parameters such as strength, dislocations, diffusivity, and microstructural stability in addition to temeprature, applied stress, and oxidation. This work focuses on predicting vacancy formation energy in nickel-based superalloys using machine learning approach. High-throughput density functional theory (DFT) calculations are performed on Ni-based alloys with the addition of various alloying elements to predict the vacancy formation energy and vacancy concentration. Machine learning is performed using various models including graph neural networks.

creep performance↗
DOE OSTI2025

Defect Stability in CdTe Based on Formation Energies and Migration Barriers

Native point defects are thought to play a key role in CdTe, either as compensation centers in intentionally doped material, as a source of conductivity in nominally undoped material, or as electron–hole recombination centers. Here, the discussion of their concentration and impact has often centered only on formation energies and transition levels. Using hybrid density functional calculations, including the effects of spin–orbit coupling (SOC), we discuss the stability of native point defects in CdTe based on their formation energies and migration barriers. We show that although Cd interstitials are the lowest energy donor defects, they are unstable at room temperature due to a low migration barrier. They are important for maintaining charge neutrality during growth or annealing at high temperatures, but once the material is brought to room temperature, they are not frozen in as often assumed and are expected to anneal out, leaving the other more stable defects to determine the conductivity. Taking this into account in the solution of the charge neutrality equation, we are able to predict the conductivity type and carrier concentrations that are in good agreement with experimental observations.

14 SOLAR ENERGY↗
NASA NTRS1987

Vacancy formation energies in II-VI semiconductors

Cation and anion vacancy formation energies are calculated for HgTe, ZnTe, CdTe, and their dilute alloys. Harrison's tight-bonding theory is extended to a cluster embedded in an extended crystal. Only neutral vacancies have been considered, and two final states for the removed atom have been addressed: a free atom in vacuum and an atom on an ideal (111) surface.

Berding, M. A.↗
DOE OSTI2023

Density functional study of atomic arrangements in CrMnFeCoNi high-entropy alloy and their impact on vacancy formation energy and segregation

Here, using the density functional theory-coupled Monte Carlo approach, we explored the chemical short-range order (SRO) and element segregation in equimolar CrMnFeCoNi alloy. We found that state-of-the-art approximation of random element distribution is only applicable at > 1100 K close to the melting temperature, while the Cr-Cr repulsion driving the system stabilization and accompanying the formation of cubic Cr sublattice, and mild Ni-Ni attraction are the most prominent pair interactions at lower temperatures. Chemical potential and vacancy formation energy calculations indicate that Cr is most sensitive to the local chemical environment, making Cr atoms most stabilized when the preferred SRO is introduced. While the vacancy formation is predicted equally probable among five constituting elements in the random solid solution, Cr and Ni atoms show the lowest vacancy formation energies in the structure with SRO. Furthermore, distinct element segregation was predicted in the vicinity of planar defects, including symmetric tilt grain boundary and stacking fault, which we correlated to the site- and chemistry-dependent atomic volume and bond lengths. It suggests that the local mechanical strain and bond energy induce the SRO development and element segregation: Namely, the system takes advantage of segregation of Ni atoms having large atomic volume or Cr-Cr pairs having elongated bond lengths to fill in the excess volume at defects that relaxes the mechanical strain field and optimizes bond energy distribution. The correlation between the SRO and properties of CrMnFeCoNi alloy needs further investigations, which is expected to greatly help understand and control the properties of high-entropy alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Machine learning substitutional defect formation energies in ABO 3 perovskites

Perovskite oxides are a promising material platform for use in a wide range of technological applications including electronics, sensors, fuel cells, and catalysis. This is owing to the extraordinary tunability of their physical and chemical properties via defect engineering. The feasibility and the stability of a defect, such as a substitutional dopant, in the host lattice is usually obtained via experiments and/or through detailed quantum mechanical calculations. Both of these conventional routes are expensive and time consuming. An alternative is a data-driven machine learning (ML)-based approach. In this work, we have applied ML techniques to identify the factors that influence defect formation energy, which is an important measure of the stability of the defects, in perovskite oxides. Using 13 elemental properties as features and random forest regression, we demonstrate a systematic approach to down-selecting from the larger set of features to those that are important, establishing a framework for accurate predictions of the defect formation energy. We quantitatively show that the most important factors that control the dopant stability are the dopant ionic size, heat of formation, effective tolerance factor, and oxidation state. Our work reveals previously unknown correlations, chemical trends, and the interplay between stability and underlying chemistries. Hence, these results showcase the efficacy of ML tools in identifying and quantifying different feature-dependencies and provide a promising route toward dopant selection in the perovskites. Herein, we have developed a framework that itself is general and can be potentially applied to other material classes.

36 MATERIALS SCIENCE↗