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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 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Vertical detachment energies of ammonia cluster anions using self-interaction-corrected methods

Systems with weakly bound extra electrons impose great challenges to semilocal density functional approximations (DFAs), which suffer from self-interaction errors. Small ammonia clusters are one such example of weakly bound anions where the extra electron is weakly bound. We applied two self-interaction correction (SIC) schemes, viz., the well-known Perdew–Zunger and the recently developed locally scaled SIC (LSIC) with the local spin density approximation (LSDA), Perdew–Burke–Ernzerhof (PBE) generalized gradient approximation (GGA), and the SCAN meta-GGA functionals to calculate the vertical detachment energies (VDEs) of small ammonia cluster anions (NH3)n–. Our results show that the LSIC significantly reduces the errors in calculations of VDE with LSDA and PBE-GGA functionals leading to better agreement with the reference values calculated with coupled cluster singles and doubles with perturbative triples [CCSD(T)]. Accurate prediction of VDE as an absolute of the highest occupied molecular orbital (HOMO) is challenging for DFAs. Our results show that VDEs estimated from the negative of HOMO eigenvalues with the LSIC-LSDA and Perdew–Zunger SIC-PBE are within 11 meV of the reference CCSD(T) results. Here, the LSIC method performs consistently well for the VDE estimates, from both the total energy differences and the absolute HOMO eigenvalues.

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

DMC-ICE13 : Ambient and high pressure polymorphs of ice from diffusion Monte Carlo and density functional theory

Ice is one of the most important and interesting molecular crystals, exhibiting a rich and evolving phase diagram. Recent discoveries mean that there are now 20 distinct polymorphs; a structural diversity that arises from a delicate interplay of hydrogen bonding and van der Waals dispersion forces. This wealth of structures provides a stern test of electronic structure theories, with Density Functional Theory (DFT) often not able to accurately characterize the relative energies of the various ice polymorphs. Thanks to recent advances that enable the accurate and efficient treatment of molecular crystals with Diffusion Monte Carlo (DMC), we present here the DMC-ICE13 dataset; a dataset of lattice energies of 13 ice polymorphs. This dataset encompasses the full structural complexity found in the ambient and high-pressure molecular ice polymorphs, and when experimental reference energies are available, our DMC results deliver sub-chemical accuracy. Using this dataset, we then perform an extensive benchmark of a broad range of DFT functionals. Of the functionals considered, revPBE-D3 and RSCAN reproduce reference absolute lattice energies with the smallest error, while optB86b-vdW and SCAN+rVV10 have the best performance on the relative lattice energies. Our results suggest that a single functional achieving reliable performance for all phases is still missing, and that care is needed in the selection of the most appropriate functional for the desired application. The insights obtained here may also be relevant to liquid water and other hydrogen-bonded and dispersion-bonded molecular crystals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Nonradiative relaxation dynamics in the [Au 25- n Ag n (SH) 18 ] -1 ( n = 1, 12, 25) thiolate-protected nanoclusters

Evaluation of the electron-nuclear dynamics and relaxation mechanisms of gold and silver nanoclusters and their alloys is important for future photocatalytic, light harvesting, and photoluminescence applications of these systems. In this work, the effect of silver doping on the nonradiative excited state relaxation dynamics of the atomically precise thiolate-protected gold nanocluster [Au 25- n Ag n (SH) 18 ] –1 ( n = 1, 12, 25) is studied theoretically. Time-dependent density functional theory is used to study excited states lying in the energy range 0.0–2.5 eV. The fewest switches surface hopping method with decoherence correction was used to investigate the dynamics of these states. The HOMO–LUMO gap increases significantly upon doping of 12 silver atoms but decreases for the pure silver nanocluster. Doped clusters show a different response for ground state population increase lifetimes and excited state population decay times in comparison to the undoped system. The ground state recovery times of the S 1 –S 6 states in the first excited peak were found to be longer for [Au 13 Ag 12 (SH) 18 ] –1 than the corresponding recovery times of other studied nanoclusters, suggesting that this partially doped nanocluster is best for preserving electrons in an excited state. The decay time constants were in the range of 2.0–20 ps for the six lowest energy excited states. Among the higher excited states, S 7 has the slowest decay time constant although it occurs more quickly than S 1 decay. Overall, these clusters follow common decay time constant trends and relaxation mechanisms due to the similarities in their electronic structures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Hydrogen Bond Benchmark: Focal‐Point Analysis and Assessment of DFT Functionals

We performed a hierarchical, convergent ab initio benchmark study and systematically analyzed the performance of density functional approximations for describing hydrogen bonds in small neutral, cationic, and anionic complexes, as well as in larger systems involving amide, urea, deltamide, and squaramide moieties. Focal point analyses (FPA), extrapolating to the ab initio limit, were carried out using correlated wave function methods up to CCSDT(Q) for the small complexes and CCSD(T) for the larger systems, together with correlation-consistent Gaussian basis sets up to the complete basis set limit. Optimized geometries and vibrational frequencies were obtained at the CCSD(T) level. The resulting FPA hydrogen-bond energies converge within a few tenths of a kcal mol −1 . These reference data were used to evaluate 60 density functionals (including 12 dispersion-corrected), spanning the local-density approximation (LDA), generalized gradient approximations (GGAs), meta-GGAs, hybrids, meta-hybrids, double-hybrids, and range-separated hybrids. Overall, the meta-hybrid M06-2X provides the best performance for both hydrogen bond energies and geometries, while the dispersion-corrected GGAs BLYP-D3(BJ) and BLYP-D4 also yield accurate hydrogen-bond data and can serve as cost-effective options for studying large and complex systems.

coupled cluster theory↗
DOE OSTI2023

Hourglass-type bulk Ni 3 d band and Ce 4 f Kondo resonance states in the potential topological Kondo semimetal CeNiSn via angle-resolved photoemission spectroscopy

The electronic structure of CeNiSn, which is a potential topological Kondo insulator and a Dirac nodal-loop semimetal, has been investigated by employing temperature (T) dependent angle-resolved photoemission spectroscopy (ARPES). The Fermi surfaces (FSs) and the band structures of CeNiSn for three orthogonal crystallographic planes are measured, in which both the very dispersive bands and the flat bands are observed, having mainly the Ni 3d character and the Ce 4f character, respectively. The measured FSs and ARPES bands agree reasonably well with the density functional theory (DFT) calculations. The Fermi-edge (E F ) photon energy (hν) map along k b (=k (010) ) shows that the metallic E F -crossing states on the (010) surface have the three-dimensional character, suggesting that the observed E F -crossing metallic states do not correspond to the topological surface states of the two-dimensional character. On the other hand, albeit weak, the features of the hourglass-type bulk band crossings are observed along SXS, with the energies and the slopes being similar to those predicted by the DFT calculations, supporting the Dirac semimetallic nature of CeNiSn. In T-dependent ARPES, the Ce 4f Kondo resonance states are clearly revealed at low T, which become much suppressed above ~80 K. This feature is consistent with the Kondo temperature of CeNiSn, estimated from its ρ(T) data. This work demonstrates the importance of the coherent Kondo states in determining the topological properties of CeNiSn.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Accelerating Multiscale Materials Modeling with Machine Learning

The focus of this project is to accelerate and transform the workflow of multiscale materials modeling by developing an integrated toolchain seamlessly combining DFT, SNAP, LAMMPS, (shown in Figure 1-1) and a machine-learning (ML) model that will more efficiently extract information from a smaller set of first-principles calculations. Our ML model enables us to accelerate first-principles data generation by interpolating existing high fidelity data, and extend the simulation scale by extrapolating high fidelity data (10 2 atoms) to the mesoscale (10 4 atoms). It encodes the underlying physics of atomic interactions on the microscopic scale by adapting a variety of ML techniques such as deep neural networks (DNNs), and graph neural networks (GNNs). We developed a new surrogate model for density functional theory using deep neural networks. The developed ML surrogate is demonstrated in a workflow to generate accurate band energies, total energies, and density of the 298K and 933K Aluminum systems. Furthermore, the models can be used to predict the quantities of interest for systems with more number of atoms than the training data set. We have demonstrated that the ML model can be used to compute the quantities of interest for systems with 100,000 Al atoms. When compared with 2000 Al system the new surrogate model is as accurate as DFT, but three orders of magnitude faster. We also explored optimal experimental design techniques to choose the training data and novel Graph Neural Networks to train on smaller data sets. These are promising methods that need to be explored in the future.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Solution–Doped Donor–Acceptor Copolymers Based on Diketopyrrolopyrrole and 3, 3'–Bis (2–(2–(2–Methoxyethoxy) Ethoxy) ethoxy)–2, 2'–Bithiophene Exhibiting Outstanding Thermoelectric Power Factors with p –Dopants

The design of polymeric semiconductors exhibiting high electrical conductivity (σ) and thermoelectric power factor (PF) will be vital for flexible large-area electronics. In this work, four polymers based on diketopyrrolopyrrole (DPP), 2,3-dihydrothieno[3,4-b][1,4]dioxine (EDOT), thieno[3,2-b]thiophene (TT), and 3, 3'-bis (2-(2-(2-methoxyethoxy) ethoxy) ethoxy)-2, 2'-bithiophene (MEET) are investigated as side-chains, with the MEET polymers newly synthesized for this study. These polymers are systematically doped with tetrafluorotetracyanoquinodimethane ( F 4 TCNQ), CF3SO3H, and the synthesized dopant Cp(CN) 3 -(COOMe) 3 , differing in geometry and electron affinity. The DPP-EDOT-based polymer containing MEET as side-chains exhibits the highest conductivity (σ) ≈700 S cm–1 in this series with the acidic dopant (CF 3 SO 3 H). This polymer also shows the lowest oxidation potential by cyclic voltammetry (CV), the strongest intermolecular interactions evidenced by differential scanning calorimetry (DSC), and has the most oxygen-based functionality for possible hydrogen bonding and ionic screening. Other polymers exhibit high σ ≈300–500 S cm–1 and power factor up to 300 µW m –1 K –2 . The mechanism of conductivity is predominantly electronic, as validated by time-dependent conductance studies and transient thermo voltage monitoring over time, including for those doped with the acid. Furthermore, these materials maintain significant thermal stability and air stability over ≈6 weeks. Density functional theory calculations reveal molecular geometries and inform about frontier energy levels. Raman spectroscopy, in conjunction with scanning electron microscopy (SEM-EDS) and x-ray diffraction, provides insight into the solid-state microstructure and degree of phase separation of the doped polymer films. Infrared spectroscopy enables this study to further quantify the degree of charge transfer from polymer to dopant.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Radionuclide incorporation in negative thermal expansion α-Zr(WO 4 ) 2 : A density functional theory study

The incorporation of uranium, plutonium and technetium in the negative thermal expansion (NTE) α-Zr (WO 4 ) 2 has been investigated within the framework of density functional theory (DFT). It is found that the vacancy formation energies of the charged vacancies are overall larger than that of its counterpart neutral Frenkel defects and Schottky defects. DFT calculations suggest that U and Pu substitutions for the Zr site are preferred in α-Zr (WO 4 ) 2 . In case of Tc substitution, both Tc(IV) for the Zr site and Tc(VII) for the W site are considered under oxygen-poor and oxygen-rich conditions, while Tc(VII) substitution can be improved significantly by including Y 2 O 3 (charge compensation).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Pseudoatom molecular dynamics plasma microfields

Spectral lines are powerful diagnostic tools for both laboratory and astrophysical plasmas, as their shape is sensitive to the plasma environment. The low-frequency component of the electric microfield is an important input for semi-analytic line broadening codes. Here, in this paper, we detail a new method of calculating plasma microfields using configuration-resolved pseudoatom molecular dynamics. This approach accounts for both quantum atomic structure and N-body effects, similar to density functional theory molecular dynamics, but with less computational cost. We present pseudoatom microfields at plasma conditions relevant for recent high energy density laboratory astrophysics experiments conducted at the Sandia Z-Machine, National Ignition Facility, and Linac Coherent Light Source. Compared to established microfield codes we find moderate deviations at solid density conditions and strong agreement at lower plasma densities.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Impact of fission product inclusion on phase development in U 3 Si 2 fuel

Due to its high thermal conductivity and uranium density, U 3 S i2 has been considered as a candidate for use as an accident tolerant fuel (ATF). In order to fully assess its suitability and performance as a fuel, the impact of fission products (FPs) on the stability and performance of U 3 Si 2 must be investigated. The interactions of FPs and U 3 Si 2 have had relatively little study until now and require experimental and computational examination. U 3 Si 2 was doped with individual FPs to explore U-Si-FP interactions and phase equilibria that may impact the performance of the ATF during irradiation. Elemental Ce, Mo, Y, or Zr were used to individually dope U 3 Si 2 at a concentration of 5 wt% FP. A diffusion couple of a 1:1 Mo:Zr alloy and U 3 Si 2 was heated to 1200 °C in order to consider the impacts of multiple FPs on the stability and structure of the fuel. Samples were characterized for FP solubility and secondary phase formation using electron microscopy, energy dispersive spectroscopy, and x-ray diffraction. First principles density functional theory calculations complemented the experimental effort to understand FP behavior. Experimental and computational findings were used in the development of a thermodynamic database containing 8 major FPs and their associated silicide phases. Finally, fuel compositions generated from depletion calculations were used to thermodynamically model the equilibrium phases of the fuel undergoing burnup.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Surface Studies of β-1,3,5,7-Tetranitro-1,3,5,7-Tetrazoctane and Pentaerythritol Tetranitrate from Density Functional Tight-Binding Calculations and Implications on Crystal Shape

Here, we use density functional tight-binding (DFTB) theory to calculate the surface energies of two energetic crystals: monoclinic β-1,3,5,7-tetranitro-1,3,5,7-tetrazoctane (β-HMX) and tetragonal pentaerythritol tetranitrate (PETN). The results are then employed to determine crystal shapes using the Bravais–Friedel–Donnay–Harker, attachment energy, and surface energy models. We find that energy-based models yield predictions in good agreement with experimental observations. Additionally, we propose a simple model that reframes surface energy as a measure of the lost intermolecular interactions during the formation of a surface from the bulk. The model accurately captures the results from the DFTB calculations and enables us to explain and predict surface energies as a function of the local molecular environment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Single-Molecule Kinetics of Styrene Hydrogenation on Silica-Supported Vanadium: The Role of Disorder for Single-Atom Catalysts

We report a theoretical approach for the study of supported atom catalysis is developed based on recent advances in the study of single-molecule kinetics. This view is particularly useful in exhibiting the role of disorder in single-atom and single-site catalysts on amorphous supports. The distribution of passage times (or waiting times) through a complex catalytic network originating from a set of coupled active sites is described by a probability distribution function (PDF), f(t), that reflects the local environment of the reaction center. An efficient algorithm is developed based on the linear algebra of the Markov transition matrix that produces f(t) or its moments. The kinetics of the hydrogenation reaction of styrene on an organovanadium(III) catalyst supported on amorphous silica is studied. A kinetic model consisting of three intertwined catalytic cycles emanating from three chemically distinct active sites is proposed to describe the chemistry. Density functional theory (DFT) calculations are employed to determine the free energy barriers of the reactions, which are used to construct the rate coefficient matrix. The disorder induced by the amorphous support material is divided into a low-dimensional short-range component reflecting the covalent structures near the reaction center and a weaker long-range component modeling the bulk randomness. The results are computed and analyzed for a wide range of concentration values and disorder scenarios. The unusual structure in the f(t) PDF is found to occur for certain cases that reveal the contribution of multiple catalytic pathways acting in concert.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Density Functional Theory Study of Iron–Oxygen Divacancies in Magnetite (Fe 3 O 4 ) and Hematite (Fe 2 O 3 )

Density functional theory (DFT) calculations are employed to investigate the formation energies, charge redistribution, and binding energies of iron–oxygen divacancies in magnetite (Fe 3 O 4 ) and hematite (Fe 2 O 3 ). For magnetite, we focus on the low-temperature phase to explore variations with local environments. Building on previous DFT calculations of the variations in formation energies for oxygen vacancies with local charge and spin order in magnetite, we extend this analysis to include octahedral iron vacancies before analyzing the iron–oxygen divacancies. We also assessed the relative stability of iron–oxygen divacancies by comparing their formation energies with those of individual vacancies. Our findings reveal a significant energetic driving force for the formation of divacancy clusters, particularly in magnetite, where divacancies in the +1 charge state exhibit formation energies comparable to those of neutral iron vacancies under oxidizing conditions. In hematite, the results indicate a strong tendency for oxygen vacancies to bind to iron vacancies. These results highlight the significance of iron–oxygen vacancy complexes in the transport properties of iron oxides, with particular relevance to diffusion mechanisms under irradiation conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2022

High-Accuracy Semiempirical Quantum Models Based on a Minimal Training Set

A great need exists for computationally efficient quantum simulation approaches that can achieve an accuracy similar to high-level theories at a fraction of the computational cost. In this regard, we have leveraged a machine-learned interaction potential based on Chebyshev polynomials to improve density functional tight binding (DFTB) models for organic materials. The benefit of our approach is two-fold: (1) many-body interactions can be corrected for in a systematic and rapidly tunable process, and (2) high-level quantum accuracy for a broad range of compounds can be achieved with ~0.3% of data required for one advanced deep learning potential. Our model exhibits both transferability and extensibility through comparison to quantum chemical results for organic clusters, solid carbon phases, and molecular crystal phase stability rankings. Overall, our efforts thus allow for high-throughput physical and chemical predictions with up to coupled-cluster accuracy for systems that are computationally intractable with standard approaches.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Acetic Acid Adsorption and Reactions on Ni(110)

Acetic acid adsorption and reactions at multiple surface coverage values on Ni(110) were studied with temperature-programmed desorption (TPD) and infrared reflection absorption spectroscopy (IRAS) at 90–500 K. The experimental measurements were interpreted with density functional theory (DFT) calculations that provided information on adsorbate geometries, energies, and vibrational modes. Below the monolayer saturation coverage of 0.36 ML at 90 K, acetic acid adsorbs mostly molecularly. Above this coverage, a physisorbed layer is formed with dimers and catemers, without detectable monomers. Dimers and catemers desorb as molecular acetic acid at 157 and 172 K, respectively. Between 90 and 200 K, the O–H bond in acetic acid breaks to form bridge-bonded bidentate acetate that becomes the dominant surface species. Desorption-limited hydrogen evolution is observed at 265 K. However, even after the acetate formation, acetic acid desorbs molecularly at 200–300 K due to recombination. Minor surface species observed at 200 K, acetyls or acetates with a carbonyl group, decompose below 350 K and generate adsorbed carbon monoxide. At 350 K, the surface likely undergoes restructuring, the extent of which increases with acetic acid coverage. The initial dominant bridge-bonded bidentate acetate species formed below 200 K remain on the surface, but they now mostly adsorb on the restructured sites. Here, the acetates and all other remaining hydrocarbon species decompose simultaneously at 425 K in a narrow temperature range with concurrent evolution of hydrogen, carbon monoxide, and carbon dioxide. Above 425 K, only carbon remains on the surface.

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

Active Learning Guided Computational Discovery of Plant-Based Redoxmers for Organic Nonaqueous Redox Flow Batteries

Organic nonaqueous redox flow batteries (ONRFBs) are promising energy storage devices due to their scalability and reliance on sourceable materials. However, finding suitable redox-active organic molecules (redoxmers) for these batteries remains a challenge. Using plant-based compounds as precursors for these redoxmers can decrease their costs and environmental toxicity. In this computational study, flavonoid molecules have been examined as potential redoxmers for ONRFBs. Flavone and isoflavone derivatives were selected as catholyte (positive charge carrier) and anolyte (negative charge carrier) molecules, respectively. To drive their redox potentials to the opposite extremes, in silico derivatization was performed using a novel algorithm to generate a library of > 40000 candidate molecules that penalizes overly complex structures. A multiobjective Bayesian optimization based active learning algorithm was then used to identify best redoxmer candidates in these search spaces. Furthermore, our study provides methodologies for molecular design and optimization of natural scaffolds and highlights the need of incorporating expert chemistry awareness of the natural products and the basic rules of synthetic chemistry in machine learning.

25 ENERGY STORAGE↗