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Results for “optimized energy density functionals”

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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

SU-F-T-191: 4D Dose Reconstruction of Intensity Modulated Proton Therapy (IMPT) Based On Breathing Probability Density Function (PDF) From 4D Cone Beam Projection Images: A Study for Lung Treatment

Purpose: With energy repainting in lung IMPT, the dose delivered is approximate to the convolution of dose in each phase with corresponding breathing PDF. This study is to compute breathing PDF weighted 4D dose in lung IMPT treatment and compare to its initial robust plan. Methods: Six lung patients were evaluated in this study. Amsterdam shroud image were generated from pre-treatment 4D cone-beam projections. Diaphragm motion curve was extract from the shroud image and the breathing PDF was generated. Each patient was planned to 60 Gy (12GyX5). In initial plans, ITV density on average CT was overridden with its maximum value for planning, using two IMPT beams with robust optimization (5mm uncertainty in patient position and 3.5% range uncertainty). The plan was applied to all 4D CT phases. The dose in each phase was deformed to a reference phase. 4D dose is reconstructed by summing all these doses based on corresponding weighting from the PDF. Plan parameters, including maximum dose (Dmax), ITV V100, homogeneity index (HI=D2/D98), R50 (50%IDL/ITV), and the lung-GTV’s V12.5 and V5 were compared between the reconstructed 4D dose to initial plans. Results: The Dmax is significantly less dose in the reconstructed 4D dose, 68.12±3.5Gy, vs. 70.1±4.3Gy in the initial plans (p=0.015). No significant difference is found for the ITV V100, HI, and R50, 92.2%±15.4% vs. 96.3%±2.5% (p=0.565), 1.033±0.016 vs. 1.038±0.017 (p=0.548), 19.2±12.1 vs. 18.1±11.6 (p=0.265), for the 4D dose and initial plans, respectively. The lung-GTV V12.5 and V5 are significantly high in the 4D dose, 13.9%±4.8% vs. 13.0%±4.6% (p=0.021) and 17.6%±5.4% vs. 16.9%±5.2% (p=0.011), respectively. Conclusion: 4D dose reconstruction based on phase PDF can be used to evaluate the dose received by the patient. A robust optimization based on the phase PDF may even further improve patient care.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Two-dimensional boron–nitrogen–carbon monolayers with tunable direct band gaps

The search for new candidate semiconductors with direct band gaps of ~1.4 eV has attracted significant attention, especially among the two-dimensional (2D) materials, which have become potential candidates for next-generation optoelectronics. In this work, we systematically studied 2D B x /2 N x /2 C 1– x (0 < x < 1) compounds in particular focus on the four stoichiometric B x /2 N x /2 C 1– x ( x = 2/3, 1/2, 2/5 and 1/3) using a recently developed global optimization method (CALYPSO) in conjunction with density functional theory. Furthermore, we examine more stoichiometries by the cluster expansion technique based on a hexagonal lattice. The results reveal that all monolayer B x /2 N x /2 C 1– x stoichiometries adopt a planar honeycomb character and are dynamically stable. Remarkably, electronic structural calculations show that most of B x /2 N x /2 C 1– x phases possess direct band gaps within the optical range, thereby they can potentially be used in high-efficiency conversion of solar energy to electric power, as well as in p–n junction photovoltaic modules. The present results also show that the band gaps of B x /2 N x /2 C 1– x can be widely tuned within the optical range by changing the concentration of carbon, thus allowing the fast development of band gap engineered materials in optoelectronics. These new findings may enable new approaches to the design of microelectronic devices.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Development of neural network force fields for corrosion studies

To fully understand the chemistry and physics of corrosion, novel methods of simulation must be developed. One approach is designing machine learning (ML) algorithms integrated with density functional theory to develop adaptive force fields to gain insight into corrosion behavior namely at the surface of metal oxides. Current methods of modeling corrosion are slow due to the computational cost of resolving both reaction mechanics and mass transport processes. Machine learning methods can be implemented to obtain structure-activity relationships at both the molecular and bulk scale while still retaining the accuracy of density functional theory (DFT) and significantly decreasing the time needed for simulations of complex chemical processes in the various environments of corrosion. Multiscale models are needed for corrosion studies to fully understand its processes not only at the atomic length scale (chemical bonding, energies, and forces), but also at the nano and meso length scales (solid-state physics and material science processes). Current methods of study include DFT, molecular dynamics, and Monte Carlo. The limitation of DFT is that only a small number of atoms or molecules can be simulated at that level of theory. Density functional theory is used to study the electronic structure of atoms and molecules, and calculate the force component of each atom. However, these calculations are limited to about 1000 atoms. Custom periodic boundary conditions (PBC) can be used to describe the various environments and defects that affect the atomic forces to produce a large data set from which a training set can be derived. Machine learning can be utilized to overcome the barrier of modeling macroscopic and multi-scale processes from ab initio calculations through the development of adaptive force fields. Local environments determine the atomic forces of a given system, therefore adaptive force fields must be created to produce reliable quantum mechanical calculations. This can be achieved by developing a learning algorithm that uses the mapped atomic forces or fingerprint as an input to produce energies and magnetic moments as output. A systematic approach was used to begin to build a data set in order to accurately describe the atomic forces in various environments. In Figure 4 below, a simple PBC cell of Fe{sub 2}O{sub 3} was first optimized. A surface optimization was performed next, followed by a hydroxylated surface optimization. Once this calculation has converged, the adsorption of halide species to the hydroxylated surface will be investigated. TensorFlow is an open source platform for machine learning developed by Google. Using a high level application program interface (API) such as Keras allows for building and training ML models easily in a number of different environments and languages. For this project, a neural network was developed within Anaconda in Python. Future Work: Further development of reference data set; Refining neural network and learning algorithm; Fingerprinting atomic environment to enable mapping of atomic force components; Choosing appropriate training set from reference data; Learning from training set and enabling non-linear mapping of training set fingerprints and the atomic forces; Estimation of uncertainty to identify ranges of outside applicability; Testing and analysis of molecular dynamic simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Accelerated screening of functional atomic impurities in halide perovskites using high-throughput computations and machine learning

The pressing need for novel materials that can serve rising demands in solar cell and optoelectronic technologies makes the nexus of halide perovskites, high-throughput computations, and machine learning, very promising. Ever increasing amounts of data on the structure, fundamental properties, and device performance of halide perovskites provide opportunities for learning chemical rules and design principles that make these materials attractive, and applying them across wide chemical spaces. In this work, we show that impurity properties of halide perovskites computed using density functional theory (DFT) can be combined with machine learning (ML) to deliver predictive models and quick identification of optoelectronically active impurity atoms. Our computation lead to the largest reported dataset of the formation energies and charge transition levels of Pb-site impurities in methylammonium lead halide (MAPbX 3 ) perovskites. Descriptors are defined to uniquely represent any impurity atom in any MAPbX 3 compound and mapped to the computed impurity properties using regression techniques such as Gaussian process regression, neural networks, and random forests. We use the best optimized predictive models to make predictions for hundreds of impurities across 9 MAPbX 3 compounds and create lists of dominating impurities, that is, impurities that can shift the equilibrium Fermi level in the perovskite as determined by native point defects. Finally, this accelerated screening powered by computations and machine learning can guide the identification of problematic impurities that may cause undesired recombination of charge carriers, as well as impurities that can be deliberately introduced to tune the perovskite conductivity and resulting photovoltaic absorption.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Pinning ångström-size solid ionic channels for rare-earth element separation

High-purity rare-earth elements are essential for modern technologies, yet current solvent extraction processes are energy-intensive and environmentally harmful because of inadequate selectivity and ligand toxicity. Although combining size exclusion and binding affinity can improve lanthanide separation, the role of long-range confinement remains underexplored. Here we report lanthanide separation in aqueous systems using extremely confined manganese oxide solid ionic channels with optimized layer spacing. Different lanthanides induce distinct solid-state phase transformations in manganese oxide, creating a strong driving force for separation. Two lanthanide groups, differing by ~1.4 Å in spacing, were identified and confirmed to be stable by density functional theory. The narrower confinement of heavier Group II lanthanides improves cross-group separation by increasing the dehydration barrier for lighter Group I lanthanides without inducing strong binding. Here, we further developed a strategy to pin the confinement dimensions and enhance same-group separation, increasing enrichment factors for La–Nd and La–Pr pairs from 1.6 ± 0.1 and 1.5 ± 0.1 to 5.4 ± 0.1 and 4.2 ± 0.1, respectively.

Chemical engineering↗
DOE OSTI2024

Melt Pool characteristics on surface roughness and printability of 316L stainless steel in laser powder bed fusion

Purpose Surface quality and porosity significantly influence the structural and functional properties of the final product. This study aims to establish and explain the underlying relationships among processing parameters, top surface roughness and porosity level in additively manufactured 316L stainless steel. Design/methodology/approach A systematic variation of printing process parameters was conducted to print cubic samples based on laser power, speed and their combinations of energy density. Melt pool morphologies and dimensions, surface roughness quantified by arithmetic mean height (Sa) and porosity levels were characterized via optical confocal microscopy. Findings The study reveals that the laser power required to achieve optimal top surface quality increases with the volumetric energy density (VED) levels. A smooth top surface (Sa < 15 µm) or a rough surface with humps at high VEDs (VED > 133.3 J/mm 3 ) can serve as indicators for fully dense bulk samples, while rough top surfaces resulting from melt pool discontinuity correlate with high porosity levels. Under insufficient VED, melt pool discontinuity dominates the top surface. At high VEDs, surface quality improves with increased power as mitigation of melt pool discontinuity, followed by the deterioration with hump formation. Originality/value This study reveals and summarizes the formation mechanism of dominant features on top surface features and offers a potential method to predict the porosity by observing the top surface features with consideration of processing conditions.

Engineering↗
DOE OSTI2018

Empowering multicomponent cathode materials for sodium ion batteries by exploring three-dimensional compositional heterogeneities

Affordable sodium ion batteries hold great promise for revolutionizing stationary energy storage technologies. Sodium layered cathode materials are usually multicomponent transition metal (TM) oxides and each TM plays a unique role in the operating cathode chemistry, e.g., redox activity, structural stabilization. Engineering the three-dimensional (3D) distribution of TM cations in individual cathode particles can take advantage of a depth-dependent charging mechanism and enable a path towards tuning local TM–O chemical environments and building resilience against cathode–electrolyte interfacial reactions that are responsible for capacity fading, voltage decay and safety hazards. In this study, we create 3D compositional heterogeneity in a ternary and biphasic (O3–P3) sodium layered cathode material (Na 0.9 Cu 0.2 Fe 0.28 Mn 0.52 O 2 ). The cells containing this material deliver stable voltage profiles, and discharge capacities of 125 mA h g –1 at C/10 with almost no capacity fading after 100 cycles and 75 mA h g –1 at 1C with negligible capacity fading after 200 cycles. The direct performance comparison shows that this material outperforms other materials with similar global compositions but different mesoscale chemical distributions. Synchrotron X-ray spectroscopy/imaging and density functional theory studies reveal depth-dependent chemical environments due to changes to factors such as charge compensation and strength of orbital hybridization. Finally, 3D spectroscopic tomography illuminates the path towards optimizing multicomponent sodium layered cathode materials, to prevent the migration of TMs upon prolonged cycling. Furthermore, the study reports an inaugural effort of multifaceted and counterintuitive investigation of sodium layered cathode materials and strongly implies that there is plenty of room at the bottom by tuning nano/meso scale chemical distributions for stable cathode chemistry.

25 ENERGY STORAGE↗
DOE OSTI2022

Crystal structure of haloxon, C 14 H 14 C l3 O 6 P

The crystal structure of haloxon has been solved and refined using synchrotron X-ray powder diffraction data, and optimized using density functional theory techniques. Haloxon crystallizes in space group P2 1 /n (#14) with a = 19.60382(6), b = 10.05473(3), c = 8.73591(2) Å, β = 92.6617(2)°, V = 1720.088(11) Å 3 , and Z = 4. The structure consists of discrete molecules. The mean planes of the fused ring systems are approximately 0–11 and 011. The rings form staggered stacks perpendicular to these planes. There are no traditional hydrogen bonds in the structure, but several C–H∙∙∙O and C–H∙∙∙Cl hydrogen bonds contribute to the crystal energy. The powder pattern has been submitted to ICDD for inclusion in the Powder Diffraction File™ (PDF®).

36 MATERIALS SCIENCE↗
DOE OSTI2024

Beyond Single-Reference Fixed-Node Approximation in Ab Initio Diffusion Monte Carlo Using Antisymmetrized Geminal Power Applied to Systems with Hundreds of Electrons

Diffusion Monte Carlo (DMC) is an exact technique to project out the ground state (GS) of a Hamiltonian. Since the GS is always bosonic, in Fermionic systems, the projection needs to be carried out while imposing antisymmetric constraints, which is a nondeterministic polynomial hard problem. In practice, therefore, the application of DMC on electronic structure problems is made by employing the fixed-node (FN) approximation, consisting of performing DMC with the constraint of having a fixed, predefined nodal surface. How do we get the nodal surface? The typical approach, applied in systems having up to hundreds or even thousands of electrons, is to obtain the nodal surface from a preliminary mean-field approach (typically, a density functional theory calculation) used to obtain a single Slater determinant. This is known as single reference. In this paper, we propose a new approach, applicable to systems as large as the C 60 fullerene, which improves the nodes by going beyond the single reference. In practice, we employ an implicitly multireference ansatz (antisymmetrized geminal power wave function constraint with molecular orbitals), initialized on the preliminary mean-field approach, which is relaxed by optimizing a few parameters of the wave function determining the nodal surface by minimizing the FN-DMC energy. We highlight the improvements of the proposed approach over the standard single-reference method on several examples and, where feasible, the computational gain over the standard multireference ansatz, which makes the methods applicable to large systems. We also show that physical properties relying on relative energies, such as binding energies, are affordable and reliable within the proposed scheme.

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

Local Chemical Clustering Enabled Ultrahigh Capacitive Energy Storage in Pb-Free Relaxors

Designing Pb-free relaxors with both a high capacitive energy density (W rec ) and high storage efficiency (η) remains a remarkable challenge for cutting-edge pulsed power technologies. Local compositional heterogeneity is crucial for achieving complex polar structure in solid solution relaxors, but its role in optimizing energy storage properties is often overlooked. Here, in this paper, we report that an exceptionally high W rec of 15.2 J cm –3 along with an ultrahigh η of 91% can be achieved through designing local chemical clustering in Bi 0.5 Na 0.5 TiO 3 –BaTiO 3 -based relaxors. A three-dimensional atomistic model derived from neutron/X-ray total scattering combined with reverse Monte Carlo method reveals the presence of subnanometer scale clustering of Bi, Na, and Ba, which host differentiated polar displacements, and confirming the prediction by density functional theory calculations. This leads to a polar state with small polar clusters and strong length and direction fluctuations in unit-cell polar vectors, thus manifesting improved high-field polarizability, steadily reduced hysteresis, and high breakdown strength macroscopically. The favorable polar structure features also result in a unique field-increased η, excellent stability, and superior discharge capacity. Our work demonstrates that the hidden local chemical order exerts a significant impact on the polarization characteristic of relaxors, and can be exploited for accessing superior energy storage performance.

25 ENERGY STORAGE↗
DOE OSTI2015

Structure of the high voltage phase of layered P2-Na 2/3-z [Mn 1/2 Fe 1/2 ]O 2 and the positive effect of Ni substitution on its stability

A combination of operando X-ray diffraction, pair distribution function (PDF) analysis coupled with electrochemical measurements and Mössbauer spectroscopy elucidates the nature of the phase transitions induced by insertion and extraction of sodium ions in P2-Na 0.67 [Ni y Mn 0.5 + y Fe 0.5 -2 y ]O 2 (y = 0, 0.10, 0.15). When phase transitions are avoided, the optimal cathode material – P2-Na 0.67 Fe 0.2 Mn 0.65 Ni 0.15 O 2 - delivers 25% more energy than the unsubstituted material, sustaining high specific energy (350 Wh kg -1 ) at moderate rates and maintains 80% of the original energy density after 150 cycles - a significant improvement in performance vs. the unsubstituted analogue. The crystal structure of the high voltage phase is solved for the first time by X-ray PDF analysis of P2-Na 0.67 -z Fe0.5Mn 0.5 O 2 (where z~0.5), revealing that migration of the transition metals – particularly Fe 3+ - into tetrahedral sites in the interlayer space occurs at high potential. This results in new short range order between two adjacent layers. Although the transition metal migration is reversible as proven by electrochemical performance, it induces a large disfavourable cell polarization. The deleterious high voltage transition is mitigated by substitution of Fe 3+ by Mn 4+ /Ni 2+ , giving rise to better cycling performance. Moreover, as demonstrated by 57 Fe Mössbauer spectroscopy, the much lower ratio of Fe 4+ O6 to Fe 3+ O 6 observed systematically across the range of Ni content – compared to the values expected from a purely ionic model – suggests redox activity involves the O-2p orbitals owing to their overlap with the transition metal-3d orbitals.

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

High-Luminance LED Platform for Improved Efficacy in Directional Applications (Final Technical Report)

In this project, Lumileds developed a platform of high-luminance LEDs and LED light engines to increase efficacy and reduce energy consumption of directional lighting applications. The platform was developed through innovations in three key areas: (1) epi and device architectures optimized for high drive current density, breaking through the tradeoff in efficacy (lm/W) vs. emittance (lm/mm 2 ) exhibited by state-of-the-art products; (2) a compact chip-scale package allowing high packing density and thus high overall luminance in multi-emitter arrays; and (3) phosphor technology to enable correlated color temperature (CCT) tuning in multi-emitter arrays with optimized LED utilization, efficacy, color uniformity and color quality. Directional indoor and outdoor lighting applications make up a major portion of the total lighting energy consumption in the U.S, accounting for >40% of the energy savings potential of solid-state lighting. Success of this project helps accelerate the realization of these energy savings both through higher system efficacy and adoption due to new functionality, and thus contributes significantly to realizing the DOE Lighting program goals.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

PV Degradation Modeling: Applying Geospatial Workflows with "PVDeg"

Accurate degradation modeling is essential for predicting photovoltaic (PV) module performance, estimating longevity and informing design decisions. With degradation rates varying significantly by location, geospatial analysis is critical for PV and broader applications, such as agrivoltaics, weathering and environmental data analysis. This work presents PVDeg, an open-source tool designed for geospatial degradation analysis. PVDeg integrates meteorological data from global sources, including the National Solar Radiation Database (NSRDB) and Photovoltaic Geographical Information System (PVGIS), with degradation models. The toolkit enables users to customize geospatial workflows by integrating weather data, material parameters, and user-defined Python functions. It facilitates accelerated downloads of NSRDB and PVGIS datasets and optimizes geospatial point selection to preserve data density in regions of interest. Additionally, PVDeg provides a local database for storage and spatial queries, supporting large-scale analyses without the need for high-performance computing (HPC) resources. PVDeg provides a foundational workflow that extends its utility beyond PV applications, enabling researchers to analyze geospatial processes across discipline.

14 SOLAR ENERGY↗
DOE OSTI2015

Crystal orbital study on the double walls made of nanotubes encapsulated inside zigzag carbon nanotubes

The structure stabilities and electronic properties are investigated by using ab initio self-consistent-field crystal orbital method based on density functional theory for the one-dimensional (1D) double-wall nanotubes made of n-gon SiO{sub 2} nanotubes encapsulated inside zigzag carbon nanotubes. It is found that formation of the combined systems is energetically favorable when the distance between the two constituents is around the Van der Waals scope. The obtained band structures show that all the combined systems are semiconductors with nonzero energy gaps. The frontier energy bands (the highest occupied band and the lowest unoccupied band) of double-wall nanotubes are mainly derived from the corresponding carbon nanotubes. The mobilities of charge carriers are calculated to be within the range of 10{sup 2}–10{sup 4} cm{sup 2} V{sup −1} s{sup −1} for the hybrid double-wall nanotubes. Young’s moduli are also calculated for the combined systems. For the comparison, geometrical and electronic properties of n-gon SiO{sub 2} nanotubes are also calculated and discussed. - Graphical abstract: Structures and band structures of the optimum 1D Double walls nanotubes. The optimized structures are 3-gon SiO2@(15,0), 5-gon SiO2@(17,0), 6-gon SiO2@(18,0) and 7-gon SiO2@(19,0). - Highlights: • The structure and electronic properties of the 1D n-gon SiO{sub 2}@(m,0)s are studied using SCF-CO method. • The encapsulation of 1D n-gon SiO{sub 2} tubes inside zigzag carbon nanotubes can be energetically favorable. • The 1D n-gon SiO{sub 2}@(m,0)s are all semiconductors. • The mobility of charge carriers and Young’s moduli are calculated.

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

Towards defect engineering in hexagonal MoS 2 nanosheets for tuning hydrogen evolution and nitrogen reduction reactions

Combined computational and experimental approaches were used to evaluate defective 2H-MoS 2 nanosheets for their activity and selectivity for hydrogen evolution reaction (HER) and nitrogen reduction reaction (NRR). Density functional theory calculations were used to understand the relationship between HER and NRR activity on the ideal basal MoS 2 plane, seven grain-boundaries, ten single-/few-atom vacancies and anti-sites, and zigzag and armchair edge sites. The results confirm that 2H-MoS 2 should contain several defects with high activity for HER: armchair and zigzag edges, VS vacancy, MoS 2 anti-site, and S-S and Mo-Mo grain boundaries. Considering Gibbs free energy change for all the steps in the NRR mechanism and kinetic barriers for a key NRR step, we have found that activation of perspective NRR selective sites in 2H-MoS 2 , namely V MoS6 and clusters of S-vacancies, would require large overpotential, conditions at which HER dominates. The DFT conclusions are supported by the electrochemical studies of NRR activity and selectivity under aqueous conditions, which show an increase in NRR activity but a decrease in Faradaic efficiency as applied cell potential becomes more negative. The results of this work therefore highlight the challenges in activating natural 2H-MoS 2 for NRR, which would require additional material engineering or reaction condition optimization as a way to suppress HER, decrease the NRR overpotential or preferentially both.

2H-MoS2↗
DOE OSTI2021

Actinide Molten Salts: A Machine-Learning Potential Molecular Dynamics Study

We know that actinide molten salts represent a class of important materials in nuclear energy. Understanding them at a molecular level is critical to proper and optimal design of relevant technological applications. Yet, owing to the complexity of electronic structure due to the 5f orbitals, computational studies of heavy elements in condensed phases using ab initio potentials to study the structure and dynamics of these elements embedded in molten salts are difficult. This lack of efficient computational protocols makes it difficult to obtain information on properties that require extensive statistical sampling like transport. To tackle this problem, we adopted a machine-learning approach to study ThCl 4 -NaCl and UCl 3 -NaCl binary systems. The machine-learning potential, with the density functional theory accuracy, allows us to obtain long molecular dynamics trajectories (ns) for large systems (10 3 atoms) at a considerably low computing cost, thereby efficiently gaining information about their bonding structures, thermodynamics, and dynamics at a range of temperature. We observed a considerable change in the coordination environments of actinide elements and their characteristic coordination-sphere lifetime. Our study also suggests that actinides in molten salts may not follow well known entropy-scaling laws.

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

Defect Chemistry and Hydrogen Transport in La/Sr-Based Oxyhydrides

Oxyhydrides in the series La 2 – y Sr y LiH 1 + y O 3 – y are attractive materials for solid-state hydride electrolytes. However, their chemical stability is poor, and thus far only La 2 LiHO 3 (y = 0) has been demonstrated in a working fuel cell. Using a first-principles approach to study defect chemistry and stability, we find that La 2 LiHO 3 can be stabilized over a range of H-rich, O-poor chemical conditions, while Sr 2 LiH 3 O has low stability. Here, defects are critical for ionic transport and for stability, particularly in Sr 2 LiH 3 O, in which anion antisites and H vacancies readily form. In fact, we show that the ground-state structure of Sr 2 LiH 3 O is inherently disordered. La 2 LiHO 3 has low conductivity, which we connect to the high formation energy of defects such as VO and OH that are necessary for hydride conduction. When Sr 2 LiH 3 O is grown under carefully selected conditions, V H + defects can be made prevalent; these defects give rise to much higher ionic conductivity than can be obtained in La 2 LiHO 3 . In general, we show that the choice of synthesis conditions is vital when seeking to optimize the stability and ionic conductivity of these oxyhydrides, thereby tailoring them for use in solid-state hydrogen fuel cells.

08 HYDROGEN↗
DOE OSTI2014

Modeling Excited States in TiO2 Nanoparticles: On the Accuracy of a TD-DFT Based Description

We have investigated the suitability of Time-Dependent Density Functional Theory (TD-DFT) to describe vertical low-energy excitations in naked and hydrated titanium dioxide nanoparticles through a comparison with results from Equation-of-Motion Coupled Cluster (EOM-CC) quantum chemistry methods. We demonstrate that for most TiO2 nanoparticles TD-DFT calculations with commonly used exchange-correlation (XC-)potentials (e.g. B3LYP) and EOM-CC methods give qualitatively similar results. Importantly, however, we also show that for an important subset of structures, TD-DFT gives qualitatively different results depending upon the XC-potential used and that in this case only TD-CAM-B3LYP and TD-BHLYP calculations yield results that are consistent with those obtained using EOM-CC theory. Moreover, we demonstrate that the discrepancies for such structures arise from a particular combination of defects, excitations involving which are charge-transfer excitations and hence are poorly described by XC-potentials that contain no or low fractions of Hartree-Fock like exchange. Finally, we discuss that such defects are readily healed in the presence of ubiquitously present water and that as a result the description of vertical low-energy excitations for hydrated TiO2 nanoparticles is hence non-problematic.

calculations↗