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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

ABINIT: Overview and focus on selected capabilities

ABINIT is probably the first electronic-structure package to have been released under an open-source license about 20 years ago. It implements density functional theory, density-functional perturbation theory (DFPT), many-body perturbation theory (GW approximation and Bethe–Salpeter equation), and more specific or advanced formalisms, such as dynamical mean-field theory (DMFT) and the “temperature-dependent effective potential” approach for anharmonic effects. Relying on planewaves for the representation of wavefunctions, density, and other space-dependent quantities, with pseudopotentials or projector-augmented waves (PAWs), it is well suited for the study of periodic materials, although nanostructures and molecules can be treated with the supercell technique. The present article starts with a brief description of the project, a summary of the theories upon which ABINIT relies, and a list of the associated capabilities. It then focuses on selected capabilities that might not be present in the majority of electronic structure packages either among planewave codes or, in general, treatment of strongly correlated materials using DMFT; materials under finite electric fields; properties at nuclei (electric field gradient, Mössbauer shifts, and orbital magnetization); positron annihilation; Raman intensities and electro-optic effect; and DFPT calculations of response to strain perturbation (elastic constants and piezoelectricity), spatial dispersion (flexoelectricity), electronic mobility, temperature dependence of the gap, and spin-magnetic-field perturbation. The ABINIT DFPT implementation is very general, including systems with van der Waals interaction or with noncollinear magnetism. Community projects are also described: generation of pseudopotential and PAW datasets, high-throughput calculations (databases of phonon band structure, second-harmonic generation, and GW computations of bandgaps), and the library libpaw. ABINIT has strong links with many other software projects that are briefly mentioned.

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

Re-examining the silicon self-interstitial charge states and defect levels: A density functional theory and bounds analysis study

The self-interstitial atom (SIA) is one of two fundamental point defects in bulk Si. Isolated Si SIAs are extremely difficult to observe experimentally. Even at very low temperatures, they anneal before typical experiments can be performed. Given the challenges associated with experimental characterization, accurate theoretical calculations provide valuable information necessary to elucidate the properties of these defects. Previous studies have applied Kohn–Sham density functional theory (DFT) to the Si SIA, using either the local density approximation or the generalized gradient approximation to the exchange-correlation (XC) energy. The consensus of these studies indicates that a Si SIA may exist in five charge states ranging from -2 to +2 with the defect structure being dependent on the charge state. This study aims to re-examine the existence of these charge states in light of recently derived “approximate bounds” on the defect levels obtained from finite-size supercell calculations and new DFT calculations using both semi-local and hybrid XC approximations. We conclude that only the neutral and +2 charge states are directly supported by DFT as localized charge states of the Si SIA. Within the current accuracy of DFT, our results indicate that the +1 charge state likely consists of an electron in a conduction-band-like state that is coulombically bound to a +2 SIA. Furthermore, the -1 and -2 charge states likely consist of a neutral SIA with one and two additional electrons in the conduction band, respectively.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Toward a systematic improvement of the fixed-node approximation in diffusion Monte Carlo for solids—A case study in diamond

While Diffusion Monte Carlo (DMC) is in principle an exact stochastic method for ab initio electronic structure calculations, in practice, the fermionic sign problem necessitates the use of the fixed-node approximation and trial wavefunctions with approximate nodes (or zeros). This approximation introduces a variational error in the energy that potentially can be tested and systematically improved. Here, we present a computational method that produces trial wavefunctions with systematically improvable nodes for DMC calculations of periodic solids. These trial wavefunctions are efficiently generated with the configuration interaction using a perturbative selection made iteratively (CIPSI) method. A simple protocol in which both exact and approximate results for finite supercells are used to extrapolate to the thermodynamic limit is introduced. This approach is illustrated in the case of the carbon diamond using Slater–Jastrow trial wavefunctions including up to one million Slater determinants. Fixed-node DMC energies obtained with such large expansions are much improved, and the fixed-node error is found to decrease monotonically and smoothly as a function of the number of determinants in the trial wavefunction, a property opening the way to a better control of this error. The cohesive energy extrapolated to the thermodynamic limit is in close agreement with the estimated experimental value. Interestingly, this is also the case at the single-determinant level, thus, indicating a very good error cancellation in carbon diamond between the bulk and atomic total fixed-node energies when using single-determinant nodes.

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

High electron mobility of Al x Ga1− x N evaluated by unfolding the DFT band structure

We calculate the alloy-disorder-limited electron mobility of AlxGa1−xN from first principles. AlxGa1−xN is a technologically important ultra-wide-bandgap alloy with promise in light emitting diodes and high-power transistors. Alloying introduces statistical disorder, which causes electrons to scatter between different crystal-momentum states, leading to a reduction in mobility for intermediate alloy compositions. The corresponding lifetime, which appears as an energy broadening in the band structure, can be evaluated by unfolding the band structure from the supercell basis to the primitive-cell basis. We fit the first-principles band broadening with a model scattering potential and evaluate the low-field electron mobility using the semiclassical Boltzmann transport equation in the relaxation-time approximation. Our calculated mobility is in agreement with experimental values. We also find the lowest alloy-scattering electron mobility (total electron mobility) across the entire composition range to be 186 cm2/V s (136 cm2/V s), which is comparable to the highest electron mobility predicted in the competitor system, β-(AlxGa1−x)2O3. Our results elucidate the intrinsic limits imposed by alloy disorder on electron transport in AlxGa1−xN.

Pant, Nick (ORCID:0000000254506045)↗
DOE OSTI2022

Bandgap analysis and carrier localization in cation-disordered ZnGeN 2

The bandgap of ZnGeN 2 changes with the degree of cation site disorder and is sought in light emitting diodes for emission at green to amber wavelengths. By combining the perspectives of carrier localization and defect states, we analyze the impact of different degrees of disorder on electronic properties in ZnGeN 2 , addressing a gap in current studies, which largely focus on dilute or fully disordered systems. The present study demonstrates changes in the density of states and localization of carriers in ZnGeN 2 calculated using bandgap-corrected density functional theory and hybrid calculations on partially disordered supercells generated using the Monte Carlo method. We use localization and density of states to discuss the ill-defined nature of a bandgap in a disordered material and identify site disorder and its impact on the structure as a mechanism controlling electronic properties and potential device performance. Decreasing the order parameter results in a large reduction of the bandgap. The reduction in bandgap is due, in part, to isolated, localized states that form above the valence band continuum associated with nitrogen coordinated by more zinc than germanium. The prevalence of defect states in all but the perfectly ordered structure creates challenges for incorporating disordered ZnGeN 2 into optical devices, but the localization associated with these defects provides insight into the mechanisms of electron/hole recombination in the material.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Structure optimization with stochastic density functional theory

Linear-scaling techniques for Kohn–Sham density functional theory are essential to describe the ground state properties of extended systems. Still, these techniques often rely on the localization of the density matrix or accurate embedding approaches, limiting their applicability. In contrast, stochastic density functional theory (sDFT) achieves linear- and sub-linear scaling by statistically sampling the ground state density without relying on embedding or imposing localization. In return, ground state observables, such as the forces on the nuclei, fluctuate in sDFT, making optimizing the nuclear structure a highly non-trivial problem. In this work, we combine the most recent noise-reduction schemes for sDFT with stochastic optimization algorithms to perform structure optimization within sDFT. We compare the performance of the stochastic gradient descent approach and its variations (stochastic gradient descent with momentum) with stochastic optimization techniques that rely on the Hessian, such as the stochastic Broyden–Fletcher–Goldfarb–Shanno algorithm. In conclusion, we further provide a detailed assessment of the computational efficiency and its dependence on the optimization parameters of each method for determining the ground state structure of bulk silicon with varying supercell dimensions.

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

Electronic structure of boron and aluminum δ -doped layers in silicon

Recent work on atomic-precision dopant incorporation technologies has led to the creation of both boron and aluminum δ-doped layers in silicon with densities above the solid solubility limit. For this study, we use density functional theory to predict the band structure and effective mass values of such δ layers, first modeling them as ordered supercells. Structural relaxation is found to have a significant impact on the impurity band energies and effective masses of the boron layers, but not the aluminum layers. However, disorder in the δ layers is found to lead to a significant flattening of the bands in both cases. We calculate the local density of states and doping potential for these δ-doped layers, demonstrating that their influence is highly localized with spatial extents at most 4 nm. We conclude that acceptor δ-doped layers exhibit different electronic structure features dependent on both the dopant atom and spatial ordering. This suggests prospects for controlling the electronic properties of these layers if the local details of the incorporation chemistry can be fine-tuned.

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

Simulating charged defects at database scale

Point defects have a strong influence on the physical properties of materials, often dominating the electronic and optical behavior in semiconductors and insulators. The simulation and analysis of point defects is, therefore, crucial for understanding the growth and operation of materials, especially for optoelectronics applications. In this work, we present a general-purpose Python framework for the analysis of point defects in crystalline materials as well as a generalized workflow for their treatment with high-throughput simulations. The distinguishing feature of our approach is an emphasis on a unique, unit cell, structure-only, definition of point defects which decouples the defect definition, and the specific supercell representation used to simulate the defect. This allows the results of first-principles calculations to be aggregated into a database without extensive provenance information and is a crucial step in building a persistent database of point defects that can grow over time, a key component toward realizing the idea of a “defect genome” that can yield more complex relationships governing the behavior of defects in materials. We demonstrate several examples of the approach for three technologically relevant materials and highlight current pitfalls that must be considered when employing these methodologies as well as their potential solutions.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Phonon Olympics: Phonon property and lattice thermal conductivity benchmarking from open-source packages

Three widely used open-source packages for determining phonon properties and lattice thermal conductivities (ALAMODE, phono3py, and ShengBTE) are benchmarked by teams of expert users and the package developers. The phonons for Ge, RbBr, monolayer MoSe 2 , and AlN are modeled at zero temperature, and they scatter through three-phonon and phonon-isotope processes, with thermal conductivities obtained from the linearized Peierls–Boltzmann transport equation with input from density functional theory calculations. Over a wide range of temperatures, the thermal conductivities calculated by the teams fall within at most ±15% of their mean values for each of the four materials. The phonon frequencies, obtained from the harmonic force constants, do not show large differences between the calculations, indicating that the modal heat capacities and group velocities are not responsible for the thermal conductivity variations. It is the lifetimes associated with three-phonon scattering, obtained from the cubic force constants, that drive the variations. The many decisions required to calculate the cubic force constants (e.g., supercell size, atomic displacement, neighbor cutoff, and application of symmetries) make identification of the precise origin of the thermal conductivity variations challenging. The calculated thermal conductivities do not generally show agreement with experimental measurements, which is attributed to the limitations of the density functional theory calculations. Guidance for the development of best practices is provided, which will help to standardize protocols needed for building thermal conductivity databases. The results provide a baseline for future benchmarking of other packages and more advanced calculations.

McGaughey, Alan J. H. [Carnegie Mellon Univ., Pitt↗
DOE OSTI2026

Interplay of ferromagnetic and antiferromagnetic interactions in epitaxial Co 3 ZnN

Antiperovskite nitrides with the general formula M 3 AN have attracted significant attention due to their tunable electronic and magnetic properties. Among them are many cobalt-based compounds predicted to exhibit high thermodynamic stability and intriguing magnetic behavior. Here, we report the synthesis and magnetic characterization of epitaxial Co3ZnN thin films grown by radio frequency sputtering on SrTiO 3 (STO) and MgO substrates. X-ray diffraction confirms phase-pure (00l)-oriented films with cube-on-cube epitaxy on STO, with a c-lattice parameter of 3.752 Å. Magnetic measurements reveal clear hysteresis at 2 K with a coercive field of ∼0.11 T and a small net moment of 0.108 μ B /f.u., suggesting either a canted antiferromagnetic (AFM) or ferrimagnetic (FiM) configuration. Temperature-dependent magnetization measurements show a transition near 25 K, with strong AFM interactions with Curie–Weiss temperature (Θ) = −80.13 K. Complementary density functional theory and Monte Carlo simulations indicate a ferromagnetic (FM) ground state, with the FM–AFM energy difference decreasing systematically with increasing supercell size, consistent with competition between FM and AFM/FiM interactions. These results highlight Co3ZnN as a magnetically complex antiperovskite nitride with competing exchange interactions.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Chemical bonding dictates drastic critical temperature difference in two seemingly identical superconductors

Though YB 6 and LaB 6 share the same crystal structure, atomic valence electron configuration, and phonon modes, they exhibit drastically different phonon-mediated superconductivity. YB 6 superconducts below 8.4 K, giving it the second-highest critical temperature of known borides, second only to MgB 2 . LaB 6 does not superconduct until near-absolute zero temperatures (below 0.45 K), however. Though previous studies have quantified the canonical superconductivity descriptors of YB 6 ’s greater Fermi-level (E f ) density of states and higher electron–phonon coupling (EPC), the root of this difference has not been assessed with full detail of the electronic structure. Through chemical bonding, we determine low-lying, unoccupied 4f atomic orbitals in lanthanum to be the key difference between these superconductors. These orbitals, which are not accessible in YB 6 , hybridize with π B–B bonds and bring this π-system lower in energy than the σ B–B bonds otherwise at E f . This inversion of bands is crucial: the optical phonon modes we show responsible for superconductivity cause the σ-orbitals of YB 6 to change drastically in overlap, but couple weakly to the π-orbitals of LaB 6 . These phonons in YB 6 even access a crossing of electronic states, indicating strong EPC. No such crossing in LaB 6 is observed. Finally, a supercell (the M k-point) is shown to undergo Peierls-like effects in YB 6 , introducing additional EPC from both softened acoustic phonons and the same electron-coupled optical modes as in the unit cell. Overall, we find that LaB 6 and YB 6 have fundamentally different mechanisms of superconductivity, despite their otherwise near-identity.

Science & Technology - Other Topics↗
DOE OSTI2020

Topological and thermoelectric properties of double antiperovskite pnictides

Doubling the perovskite cell (double perovskite) has been found to open new possibilities for engineering functional materials, magnetic materials in particular. This route should be applicable to the antiperovskite (aPV) class. In the pnictide based double aPV (2aPV) class introduced here magnetism is very rare, and we address them as new topological materials, possibly with thermoelectric interest. We have found that the 2aPV supercell provides a systematically larger band gap that can serve to inhibit bulk conductivity, and also large spin–orbit coupling for band inversion. We present examples from a broad study of double antiperovskites focusing on the X 6 AA'B 2 configuration, where X is the alkaline earth element and A and B are the group 5A pnictogens. We find that an 'extended s' state at the valence band minimum, described alternatively as a cation valence state or a modulated interstitial planewave state, plays a crucial role in both topological and thermoelectric properties. Several of these compounds may house topological phases, while transport calculations indicate they may also find themselves useful in thermoelectric applications.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Exploring metastable states in UO 2 using hybrid functionals and dynamical mean field theory

A detailed exploration of the f-atomic orbital occupancy space for UO2 is performed using a first principles approach based on density functional theory (DFT), employing a full hybrid functional within a systematic basis set. Specifically, the PBE0 functional is combined with an occupancy biasing scheme implemented in a wavelet-based algorithm which is adapted to large supercells. The results are compared with previous DFT + U calculations reported in the literature, while dynamical mean field theory is also performed to provide a further base for comparison. This work shows that the computational complexity of the energy landscape of a correlated f-electron oxide is much richer than has previously been demonstrated. The resulting calculations provide evidence of the existence of multiple previously unexplored metastable electronic states of UO 2 , including those with energies which are lower than previously reported ground states.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Classical signatures of quenched and thermal disorder in the dynamics of correlated spin systems

Neutron scattering is frequently used to look for evidence of features indicative of quantum-entangled phases of matter such as continua from fractionalisation or quantised excitations. However, the non-specificity of these features and difficulty of both fully quantum treatments and semiclassical models of disorder, make the diagnosis of such states problematic. Here, we demonstrate the feasibility of semiclassical treatments of disordered systems for supercells of ~10 000 spins. By examining a number of classically disordered models we show the presence of quantised excitations, broad continua and anomalous damping originating from quenched disorder or large classical degeneracies.

36 MATERIALS SCIENCE↗
DOE OSTI2025

DUNE Photon Detection System

The Deep Underground Neutrino Experiment (DUNE) aims to provide a broad physics program primarily addressed to probing CP violation in the neutrino sector and identifying the neutrino mass hierarchy. The search for proton decay, the observation of supernova neutrino bursts, and the investigation of solar neutrinos represent other additional goals of DUNE experiment, which can be enhanced by the use and the high performances of the Photon Detection System (PDS). Using the technology based on liquid argon Time Projection Chamber (LArTPC), the experiment plans to observe neutrino interactions inside detectors located 1300 km away from the Long Baseline Neutrino Facility (LBNF) at Fermi National Accelerator Laboratory (FNAL), where the neutrinos are produced. The experiment consists of two main parts, respectively the far and near detectors. The far site will comprise four detector modules. The first module is constituted by a vertical drift single-phase LArTPC, while the second module provides a horizontal drift single-phase LArTPC. The configurations of the other modules are still under definition. Neutrino detection in LArTPCs is achieved by identifying the charge and light generated from its interactions with liquid argon. The wire planes of the instrumented anode and the PDS detect these signals, respectively. The PDS in the first two modules, detailed in the present document, uses a modified version of the so-called ARAPUCA technology, named X-ARAPUCA. This system consisting of a highly reflecting box with an entrance window made by dichroic filters and wavelength shifters, creates a trap to detect the VUV (128 nm) scintillation photons. The X-ARAPUCA of the first module is called Supercell, and it has dimensions of 488×100 mm 2 , while Megacell is the second module version, with an active area of 60×60 cm 2 . This latter configuration also represents a significant technological advancement. Since half of the modules are placed on the cathode at high voltage, they are powered and read out using innovative power-over-fiber (PoF) and signal-over-fiber (SoF) technologies. Meanwhile, the other half are installed in a membrane behind the field cage, with a total transparency of around 70%.

Neutrino detectors↗
DOE OSTI2025

Defect modeling in semiconductors: the role of first principles simulations and machine learning

Abstract Point defects in semiconductors dictate their electronic and optical properties. Vacancies, interstitials, substitutional defects, and defect complexes can form in the semiconductor lattice and significantly impact its performance in applications such as solar absorption, light emission, electronics, and catalysis. Understanding the nature and energetics of point defects is essential for the design and optimization of next-generation semiconductor technologies. Here, we provide a comprehensive overview of the current state of research on point defects in semiconductors, focusing on the application of density functional theory (DFT) and machine learning (ML) in accelerating the prediction and understanding of defect properties. DFT has been instrumental in accurately calculating defect formation energies, charge transition levels, and other defect-related properties such as carrier recombination rates and lifetimes, and ion migration barriers. ML techniques, particularly neural networks, have emerged as powerful tools for enabling rapid prediction of defect properties at DFT-accuracy in order to overcome the expense of using large supercells and advanced functionals. We begin this article with a discussion of different types of point defects and complexes, their impact on semiconductor properties, and the experimental and DFT approaches typically used for their characterization. Through multiple case studies, we explore how DFT has been successfully applied to understand defect behavior across a variety of semiconductors, and how ML approaches integrated with DFT can efficiently predict defect properties and facilitate the discovery of new materials with tailored defect behavior. Overall, the advent of ‘DFT+ML’ promises to drive advancements in semiconductor technology, catalysis, and renewable energy applications, paving the way for the development of high-performance semiconductors which are defect-tolerant or have desirable dopability.

Rahman, Md Habibur (ORCID:000000027705984X)↗
DOE OSTI2023

Thermokinetics of point defects in α-Fe 2 O 3

Abstract Point defect formation and migration in oxides governs a wide range of phenomena from corrosion kinetics and radiation damage evolution to electronic properties. In this study, we examine the thermodynamics and kinetics of anion and cation point defects using density functional theory in hematite ( α -Fe 2 O 3 ), an important iron oxide highly relevant in both corrosion of steels and water-splitting applications. These calculations indicate that the migration barriers for point defects can vary significantly with charge state, particularly for cation interstitials. Additionally, we find multiple possible migration pathways for many of the point defects in this material, related to the low symmetry of the corundum crystal structure. The possible percolation paths are examined, using the barriers to determine the magnitude and anisotropy of long-range diffusion. Our findings suggest highly anisotropic mass transport in hematite, favoring diffusion along the c -axis of the crystal. In addition, we have considered the point defect formation energetics using the largest Fe 2 O 3 supercell reported to date.

36 MATERIALS SCIENCE↗