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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Calculating the hyperfine tensors for group-IV impurity-vacancy centers in diamond using hybrid density functional theory

The hyperfine interaction is an important probe for understanding the structure and symmetry of defects in a semiconductor. Density functional theory has shown that it can provide useful first-principles predictions for both the hyperfine tensor and the hyperfine constants that arise from it. Recently there has been great interest in using group-IV impurity-vacancy color centers XV – (where X = Si, Ge, Sn, or Pb and V is a carbon vacancy) for important applications in quantum computing and quantum information science. In this paper we have calculated the hyperfine tensors for these XV – color centers using the HSE06 screened Hartree-Fock hybrid exchange-correlation functional with the inclusion of core electron spin polarization. We have compared our results to calculations which only use the PBE exchange-correlation functional without the inclusion of core electron spin polarization and we have found that our results are in very good agreement with available experimental results. Lastly, we have theoretically shown that these XV – color centers exhibit a Jahn-Teller distortion which explains the observed anisotropic distribution of the hyperfine constants among the neighboring 13 C nuclear spins.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Defect Diffusion Graph Neural Networks for Materials Discovery in High-Temperature Energy Applications

Here, the migration of crystallographic defects dictates material properties and performance for a plethora of technological applications. Density functional theory (DFT)-based nudged elastic band (NEB) calculations are a powerful computational technique for predicting defect migration activation energy barriers, yet they become prohibitively expensive for high-throughput screening of defect diffusivities. Without introducing hand-crafted (i.e., chemistry- or structure-specific) descriptors, we propose a generalized deep learning approach to train surrogate models for NEB energies of vacancy migration by hybridizing graph neural networks with transformer encoders and simply using pristine host structures as input. With sufficient training data, computationally efficient and simultaneous inference of vacancy defect thermodynamics and migration activation energies can be obtained to compute temperature-dependent vacancy diffusivities and to down-select candidates for more thorough DFT analysis or experiments. Thus, as we specifically demonstrate for potential water-splitting materials, candidates with desired defect thermodynamics, kinetics, and host stability properties can be more rapidly targeted from open-source databases of experimentally validated or hypothetical materials.

14 SOLAR ENERGY↗
DOE OSTI2025

Multiple Carrier Generation at an Exceptionally Low Energy Threshold

Multiple carrier generation (MCG), a process wherein two or more carriers are generated from a single high-energy absorbed photon, holds immense promise for quantum sensing, metrology, low-threshold lasers, and photovoltaics. Despite its potential, MCG has faced obstacles such as low efficiency and a high threshold photon energy at least twice the band gap (2?Eg) of the semiconductor, limiting its application only to a class of materials with low Eg. Here, we present a new approach that overcomes this limitation by leveraging carrier-donor scattering to excite secondary electrons from donor states strategically positioned below the conduction band. Our method relies on strong Coulomb interaction, reduced dielectric screening, slow hot carrier cooling, and strictly follows the energy conservation rules. We experimentally demonstrated this idea in a model system of monolayer (1L) MoS2 by exploiting electron-donating chalcogen vacancy states. We observed an exceptionally low MCG threshold of ~1.12?Eg for the first time in 1L MoS2. Remarkably, the quantum yield can be further increased to >3 by increasing the photon energy to 1.65?Eg, representing a substantial advancement over existing methods. Our findings extend the horizon of MCG into next-generation high-performance optoelectronic devices with an on-demand operating spectral range spanning from infrared to ultraviolet.

CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS↗
DOE OSTI2021

A layered nonstoichiometric lepidocrocite-type sodium titanate anode material for sodium-ion batteries

A lepidocrocite-structured sodium titanate prepared by ion-exchange of a Cs-containing precursor shows promise as an anode material for sodium ion batteries, with a discharge capacity of up to 229 mAh g -1 at an average potential of about 0.6 V vs. Na + /Na. Titanium vacancies in the metal oxide layers provide additional sites for sodium intercalation in addition to interlayer sites, which accounts for the higher capacity compared to other previously reported lepidocrocite-structured titanates. By screening a series of electrolyte formulations and binders, we were able to improve the first-cycle coulombic efficiency to 81.8% and 94.7% respectively using CMC/SBR-based and binder-free electrodes in ether electrolytes. Finally, the electrochemical consequences of short-term air-exposure on the electrodes are also discussed.

25 ENERGY STORAGE↗
DOE OSTI2020

CeTi 2 O 6 —A Promising Oxide for Solar Thermochemical Hydrogen Production

A large entropy of reduction is crucial in achieving materials capable of high-efficiency solar thermochemical hydrogen (STCH) production through two-step thermochemical water splitting cycles. We have recently demonstrated that the onsite electronic entropy of reduction attains an extreme value of 4.26 kB at 1500 K in Ce 4+ → Ce 3+ redox reactions, which explains the high performance and uniqueness of CeO 2 as an archetypal STCH material. However, ceria requires high temperatures (T > 1500 °C) to achieve a reasonable reduction extent because of its large reduction enthalpy, which is a major obstacle in practical applications. Therefore, new materials with a large entropy of reduction and lower reduction enthalpy are required. Here, we perform a systematic screening to search for Ce 4+ -based oxides which possess thermodynamics superior to CeO 2 for STCH production. We first search the Inorganic Crystal Structure Database (ICSD) and literature for Ce 4+ -based oxides and subsequently use density functional theory to compute their reduction enthalpies (i.e., oxygen vacancy formation energies). Here, we find that CeTi 2 O 6 with the brannerite structure is the most promising candidate for STCH because it possesses three essential characteristics of an STCH material: (i) a smaller reduction enthalpy compared to ceria yet large enough to split water, (ii) a high thermal stability, as reported experimentally, and (iii) a large entropy of reduction associated with Ce 4+ → Ce 3+ redox. Our proposed design strategy suggests that further exploration of Ce 4+ oxides for STCH production is warranted.

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

How Noninnocent Spectator Species Improve the Oxygen Reduction Activity of Single-Atom Catalysts: Microkinetic Models from First-Principles Calculations

Graphene-based single-atom catalysts are promising alternatives to platinum-based catalysts for fuel cell applications. Different transition metals have been screened using electronic structure methods by estimating onset potentials from the most endergonic elementary reaction step. Here, we calculate onset potentials for the oxygen reduction reaction on metal atoms embedded in N-substituted graphene di-vacancies by virtue of first-principles-informed microkinetic analysis. We find that for more oxophilic metals (Cr, Fe, Mn, and Ru), purely thermodynamic models systematically underestimate onset potentials. Furthermore, the oxophilic metals (Cr, Fe, Mn, and Ru) are oxidized under reaction conditions, leading to an increase in activity compared to their reduced state. Importantly, coadsorbed O m H n species actively participate in the reaction, which requires a dynamic treatment of spectator species. These findings highlight the limitations of thermodynamic analyses for electrocatalytic processes, which commonly assume the same oxidation state for each metal, and show that deviations between computational and experimental onset potentials cannot be solely attributed to the shortcomings of the electronic structure methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Can a deep-learning model make fast predictions of vacancy formation in diverse materials?

The presence of point defects, such as vacancies, plays an important role in materials design. Here, we explore the extrapolative power of a graph neural network (GNN) to predict vacancy formation energies. We show that a model trained only on perfect materials can also be used to predict vacancy formation energies (E vac ) of defect structures without the need for additional training data. Such GNN-based predictions are considerably faster than density functional theory (DFT) calculations and show potential as a quick pre-screening tool for defect systems. To test this strategy, we developed a DFT dataset of 530 E vac consisting of 3D elemental solids, alloys, oxides, semiconductors, and 2D monolayer materials. We analyzed and discussed the applicability of such direct and fast predictions. We applied the model to predict 192 494 E vac for 55 723 materials in the JARVIS-DFT database. Our work demonstrates how a GNN-model performs on unseen data.

2D materials↗
DOE OSTI2021

Impact of high-dose gamma-ray irradiation on electrical characteristics of N-polar and Ga-polar GaN p–n diodes

We investigate the impact of high-dose gamma-ray irradiation on the electrical performance of Ga-polar and N-polar GaN-based p-n diodes grown by metalorganic chemical vapor deposition. We compare the current density-voltage (J-V), capacitance-voltage (C-V), and circular transfer length method (CTLM) characteristics of the p-n diodes fabricated on Ga-polar and N-polar orientations before and after irradiation. The relative turn-on voltage increases for the Ga-polar diodes with increasing irradiation dose, while it increases initially and then starts to decrease for the N-polar diodes. The p-contact total resistance increases for Ga-polar and decreases for N-polar samples, which we attribute to the formation of point defects and additional Mg activation after irradiation. The J-V characteristics of most of the tested diodes recovered over time, suggesting the changes in the J-V characteristics are temporary and potentially due to metastable occupancy of traps after irradiation. X-ray photoelectron spectroscopy (XPS) and photoluminescence (PL) measurements reveal the existence of different types of initial defects and surface electronic states on Ga-polar and N-polar samples. Gallium vacancies (V Ga ) are dominant defects in Ga-polar samples, while nitrogen vacancies (V N ) are dominant in N-polar samples. The presence of a higher concentration of surface states on Ga-polar surfaces compared to N-polar was confirmed by calculating the band bending and the corresponding screening effect due to opposite polarization bound charge and ionized acceptors at the surface. The difference in surface stoichiometry in these two orientations is responsible for the different behavior in electrical characteristics after gamma-ray interactions.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Localized excitons in defective monolayer germanium selenide

Germanium selenide (GeSe) is a van der Waals–bonded layered material with promising optoelectronic properties, which has been experimentally synthesized for two-dimensional (2D) semiconductor applications. In the monolayer, due to reduced dimensionality and, thus, screening environment, perturbations such as the presence of defects have a significant impact on its properties. We apply density functional theory and many-body perturbation theory to understand the electronic and optical properties of GeSe containing a single selenium vacancy in the –2 charge state. We predict that the vacancy results in midgap “trap states” that strongly localize the electron and hole density and lead to sharp, low-energy optical absorption peaks below the predicted pristine optical gap. Analysis of the exciton wave function reveals that the 2D Wannier-Mott exciton of the pristine material monolayer is strongly modified by the presence of the defect. The lowest-energy exciton wave function is highly localized around the defect, with the Bohr radius reduced by a factor of 4 when compared to the pristine material and a dipole moment along the out-of-plane axis due to the defect-induced symmetry breaking. Altogether, these results suggest that the vacancy significantly modifies the excitonic properties of the system, demonstrating the importance of considering defects in the context of material design.

2-dimensional systems↗
DOE OSTI2020

Assessing the roles of Cu- and Ag-deficient layers in chalcopyrite-based solar cells through first principles calculations

Chalcopyrites are a demonstrated material platform for realizing efficient thin-film photovoltaics, with the most well known Cu(In,Ga)Se 2 (CIGS)-based solar cells exceeding 23%. Several factors, including flexibility in tuning the absorber bandgap, enhanced surface treatments, and the electrically benign nature of common defects are responsible for the existing high performance and future promise in chalcopyrite-based photovoltaic devices. The introduction of Cu-poor phases (also known as ordered-vacancy compounds or OVCs) between the absorber and buffer layers in CIGS solar cells is known to enhance device performance; however, the overall properties and role of OVCs remain poorly understood. Using first principles calculations based on the density functional theory with screened hybrid functionals, here we explore the electronic structure and stability of OVCs and their band offsets with defect-free chalcopyrite layers in Cu- and Ag-based compounds (ABX 2 where A=Cu, Ag; B=In, Ga, Al; and X=S, Se). Using AB 3 X 5 and AB 5 X 8 stoichiometries as model OVC systems, we report on the variation of the bandgap with the A/B ratio and discuss the trends in other Cu- and Ag-based chalcopyrites beyond CuInSe 2 . We find that the valence and conduction bands are lower in energy in OVCs with respect to the parent ABX 2 chalcopyrite owing to a reduced p–d interaction between X and A atoms. We additionally perform device-level simulations to assess the implications of the results, finding that the valence band offsets of OVCs are favorable, while the conduction band offsets of chalcopyrites beyond CuInSe 2 -based absorbers may be detrimental in conventional solar cell device designs.

14 SOLAR ENERGY↗
DOE OSTI2020

α-U and ω-UZr 2 in neutron irradiated U-10Zr annular metallic fuel

Here, to develop metallic fuel with ultra-high burnup of 30%-40%, an annular U-10Zr fuel with 55% smear density was fabricated through a casting route and irradiated at the Advanced Test Reactor at Idaho National Laboratory. The annular fuel design also serves as a demonstration of the feasibility to replace sodium bond with a helium bond to benefit the geological disposal of irradiated fuel, cut the cost of fuel fabrication, and boost the overall metallic fuel economy. This paper reports the results from transmission electron microscopy (TEM) based post-irradiation examination of this fuel type irradiated to a burnup of 3.3% fissions per initial heave metal atoms for initial screening purpose. After irradiation, the initial U-10Zr separated into an a-U annular region and an UZr 2+x center region with nanoscale spinodal decomposed microstructure. Because of the provided large amount of coherent interface areas, the fission gas atoms and vacancies generated in UZr 2+x phase are possibly pinned at the interface areas, leading to 20 times smaller fission gas bubbles than those in the neighboring a-U. The large bubbles in a-U become connected and merged into large pores that provide fast release paths for fission gas which prevents further fuel swelling. The fuel center still has open space to accommodate further fuel swelling from solid fission products at higher burnup. Other neutron irradiation induced phase and microstructure change are also characterized and compared with traditional solid fuel designs.

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 OSTI2024

Accelerating defect predictions in semiconductors using graph neural networks

First-principles computations reliably predict the energetics of point defects in semiconductors but are constrained by the expense of using large supercells and advanced levels of theory. Machine learning models trained on computational data, especially ones that sufficiently encode defect coordination environments, can be used to accelerate defect predictions. Here, we develop a framework for the prediction and screening of native defects and functional impurities in a chemical space of group IV, III–V, and II–VI zinc blende semiconductors, powered by crystal Graph-based Neural Networks (GNNs) trained on high-throughput density functional theory (DFT) data. Using an innovative approach of sampling partially optimized defect configurations from DFT calculations, we generate one of the largest computational defect datasets to date, containing many types of vacancies, self-interstitials, anti-site substitutions, impurity interstitials and substitutions, as well as some defect complexes. We applied three types of established GNN techniques, namely crystal graph convolutional neural network, materials graph network, and Atomistic Line Graph Neural Network (ALIGNN), to rigorously train models for predicting defect formation energy (DFE) in multiple charge states and chemical potential conditions. We find that ALIGNN yields the best DFE predictions with root mean square errors around 0.3 eV, which represents a prediction accuracy of 98% given the range of values within the dataset, improving significantly on the state-of-the-art. We further show that GNN-based defective structure optimization can take us close to DFT-optimized geometries at a fraction of the cost of full DFT. The current models are based on the semi-local generalized gradient approximation-Perdew–Burke–Ernzerhof (PBE) functional but are highly promising because of the correlation of computed energetics and defect levels with higher levels of theory and experimental data, the accuracy and necessity of discovering novel metastable and low energy defect structures at the PBE level of theory before advanced methods could be applied, and the ability to train multi-fidelity models in the future with new data from non-local functionals. The DFT-GNN models enable prediction and screening across thousands of hypothetical defects based on both unoptimized and partially optimized defective structures, helping identify electronically active defects in technologically important semiconductors.

Rahman, Md Habibur (ORCID:000000027705984X)↗
DOE OSTI2026

Entropy-defect synergy for dual luminescence mechanism in spinel: Time-resolved anti-counterfeiting and fingerprint visualization

Multimodal luminescent materials, while promising for anti-counterfeiting, often lack dynamic time-dependent responses and controllable spatial distribution, limiting their encryption capabilities in the spatiotemporal dimension. Here, this work presents a coordinated control strategy based on entropy and defect engineering, and uses a backpropagation (BP) neural network for material screening to successfully prepare spinel Mg 0.8 (Fe 0.04 Co 0.04 Ni 0.04 Cu 0.04 Zn 0.04 )Cr 2 O 4 (MgA 5 CO) phosphors with time-dependent dynamic luminescence behavior. This phosphor simultaneously activated the d-d transition luminescence (∼618 nm) derived from Co 2+ /Cr 3+ and the defect luminescence (∼398 nm) related to zinc vacancies (V Zn ) in a single-phase solid solution. The phosphor exhibits a time-dependent color evolution from pink to purple under fixed-wavelength excitation, due to the different excited-state dynamics and decay lifetimes associated with the d-d transition and defect luminescence. Structural characterization and spectral analysis confirmed the existence of V Zn and its significant role in defect luminescence process. The fluorescent and dynamic luminescent properties of entropy-based spinel oxide enable its use in advanced anti-counterfeiting applications like fingerprint recognition and color-changing dedicated anti-counterfeiting mark, showing promise in high-end and time-dynamic anti-counterfeiting fields. This research not only developed a new type of fluorescent dynamic anti-counterfeiting material, but also provided a new idea for constructing advanced optical functional materials with multiple luminescence mechanisms.

Defect project↗
DOE OSTI2021

Auger electron angular distributions following excitation or ionization from the Xe 3d and F 1s levels in xenon difluoride

Linearly polarized synchrotron radiation has been used to record polarization dependent, non-resonant Auger electron spectra of XeF 2 , encompassing the bands due to the xenon M 45 N 1 N 45 , M 45 N 23 N 45 , M 45 N 45 N 45 and M 45 N 45 V and fluorine KVV transitions. Resonantly excited Auger spectra have been measured at photon energies coinciding with the Xe 3d 5/2 → σ* and the overlapped Xe 3d 3/2 /F 1s → σ* excitations in XeF 2 . The non-resonant and resonantly excited spectra have enabled the Auger electron angular distributions, as characterized by the β A parameter, to be determined for the M 45 N 45 N 45 transitions. In the photon energy range over which the Auger electron angular distributions were measured, theoretical results indicate that transitions into the εf continuum channel dominate the Xe 3d photoionization in XeF 2 . In this limit, the theoretical value of the atomic alignment parameter (A 20 ) characterizing the core ionized state becomes constant. This theoretical value has been used to obtain the Auger electron intrinsic anisotropy parameters (α 2 ) from the β A parameters extracted from our non-resonant Auger spectra. For a particular Auger transition, the electron kinetic energy measured in the resonantly excited spectrum is higher than that in the directly ionized spectrum, due to the screening provided by the electron promoted into the σ* orbital. The interpretation of the F KVV Auger band in XeF 2 has been discussed in relation to previously published one-site populations of the doubly charged ions (XeF 2 2+ ). Here, the experimental results show that the ionization energies of the doubly charged states predominantly populated in the decay of a vacancy in the F 1s orbital in XeF 2 tend to be higher than those populated in the decay of a vacancy in the Xe 4d level in XeF 2 .

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

Nanoscale electric field imaging with an ambient scanning quantum sensor microscope

Nitrogen-vacancy (NV) center in diamond is a promising quantum sensor with remarkably versatile sensing capabilities. While scanning NV magnetometry is well-established, NV electrometry has been so far limited to bulk diamonds. Here we demonstrate imaging external alternating (AC) and direct (DC) electric fields with a single NV at the apex of a diamond scanning tip under ambient conditions. A strong electric field screening effect is observed at low frequencies. We quantitatively measure its frequency dependence and overcome this screening by mechanically oscillating the tip for imaging DC fields. Our scanning NV electrometry achieved an AC E-field sensitivity of 26 mV μm -1 Hz -1/2 , a DC E-field gradient sensitivity of 2 V μm -2 Hz -1/2 , and sub-100 nm resolution limited by the NV-sample distance. Our work represents an important step toward building a scanning-probe-based multimodal quantum sensing platform.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Surface Cooper-Pair Spin Waves in Triplet Superconductors

Here, we study the electrodynamics of spin triplet superconductors including dipolar interactions, which give rise to an interplay between the collective spin dynamics of the condensate and orbital Meissner screening currents. Within this theory, we identify a class of spin waves that originate from the coupled dynamics of the spin-symmetry breaking triplet order parameter and the electromagnetic field. In particular, we study magnetostatic spin wave modes that are localized to the sample surface. We show that these surface modes can be excited and detected using experimental techniques such as microwave spin wave resonance spectroscopy or nitrogen-vacancy magnetometry, and propose that the detection of these modes offers a means for the identification of spin triplet superconductivity.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Data-driven design of novel halide perovskite alloys

The great tunability of the properties of halide perovskites presents new opportunities for optoelectronic applications as well as significant challenges associated with exploring combinatorial chemical spaces. Here, in this work, we develop a framework powered by high-throughput computations and machine learning for the design and prediction of mixed cation halide perovskite alloys. In a chemical space of ABX 3 perovskites with a selected set of options for A, B, and X species, pseudo-cubic structures with B-site mixing are simulated using density functional theory (DFT) and several properties are computed, including stability, lattice constant, band gap, vacancy formation energy, refractive index, and optical absorption spectrum, using both semi-local and hybrid functionals. Neural networks (NN) are used to train predictive models for every property using tabulated elemental properties of A, B, and X site species as descriptors. Starting from a DFT dataset of 229 points, we use the trained NN models to predict the structural, energetic, electronic, and optical properties of an extensive dataset of 17955 compounds, and perform high-throughput screening in terms of stability, band gap, and defect tolerance, to obtain 392 promising compounds that are ranked as potential absorbers according to their photovoltaic figure of merit. Compositional trends in the screened set of attractive mixed cation halide perovskites are revealed and additional computations are performed on selected compounds. The data-driven design framework developed here is promising for designing novel mixed compositions and can be extended to a wider perovskite chemical space in terms of A, B, and X species, different kinds of mixing at the A, B, or X sites, non-cubic phases, and other properties of interest.

36 MATERIALS SCIENCE↗