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Results for “Density functional theory development”

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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Influence of substituents on the vectorial difference static dipole upon excitation in synthetic bacteriochlorins

Organic dye aggregates have been shown to exhibit exciton delocalization in natural and synthetic systems. Such dye aggregates show promise in the emerging area of quantum information science (QIS). We believe that the difference static dipole (Δd) is an essential dye parameter in the development of molecular QIS systems. However, a foundational understanding of the structural factors influencing Δd remains elusive. Bacteriochlorins play a vital role in photosynthesis due to their exceptional photophysical properties. Therefore, bacteriochlorins are particularly suitable dyes for the construction of aggregate systems for QIS. Synthetic bacteriochlorins further offer stability and tunability via chemical modifications. Here, the influence of substituents on the Δd of monomeric (non-aggregated) dyes was investigated via density functional theory (DFT) and time-dependent (TD-)DFT in a set of 5-methoxybacteriochlorins progressively substituted with ethynyl, phenyl, and phenylethynyl substituents at the 3,13- and 3,13,15-positions of the macrocycle. Symmetrically substituted 5-methoxybacteriochlorins were shown to have the largest Δd. The increase in Δd in the series of dyes was largely due to changes in orientation of the static dipole upon excitation rather than large changes in magnitude. In addition, the transition dipole (μ) and the angle between Δd and μ (ζ) were calculated. Three 5-methoxybacteriochlorins with high predicted Δd and μ were synthesized and characterized spectroscopically. The trend in Δd values empirically determined using the solvatochromic Stokes shift method was comparable with the DFT calculations.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Tailoring the structural durability and proton conductivity of electrolytes for highly fuel-flexible and reversible ceramic cells

A durable and high ionic conducting electrolyte is critical for achieving fuel-flexible and reversible protonic ceramic cells (PCCs) at reduced temperatures since the developed electrolyte materials are vulnerable to steam, CO 2 , or coking deterioration. Here, we report a fast-conducting electrolyte material BaZr 0.06 Ce 0.7 Y 0.06 Yb 0.06 Hf 0.06 Gd 0.06 O 3−δ (BZCYYbHG), demonstrating excellent durability against CO 2 and H 2 O under the realistic electrolysis operations, and a high conductivity of 0.017 S cm −1 at 550 °C for lowering the PCC operating temperature. Further, density functional theory calculations indicate that the higher configurational entropy of mixing at the B-site cations slightly reduces the hydrogen adsorption energy, suggesting a higher incorporation rate of protons or hydrogen atoms into the electrolyte bulk. Ultimately, single cells with the BZCYYbHG electrolyte deliver peak power densities of 1.39, 1.12, and 0.7 W cm −2 in H 2 , NH 3 , and wet CH 4 at 550 °C with promising durability. In addition, the PCCs achieve a current density of −1.61 A cm −2 at 1.3 V and 550 °C with a high faradaic efficiency of 91.3% at −0.5 A cm −2 , enabling stable operations in steam electrolysis mode under humid air (30% H 2 O), wet air containing CO 2 (up to 10%), and reversible cycling.

25 ENERGY STORAGE↗
DOE OSTI2023

Rare isotope-containing diamond colour centres for fundamental symmetry tests

Detecting a non-zero electric dipole moment in a particle would unambiguously signify physics beyond the Standard Model. A potential pathway towards this is the detection of a nuclear Schiff moment, the magnitude of which is enhanced by the presence of nuclear octupole deformation. However, due to the low production rate of isotopes featuring such ‘pear-shaped’ nuclei, capturing, detecting and manipulating them efficiently is a crucial prerequisite. Incorporating them into synthetic diamond optical crystals can produce defects with defined, molecule-like structures and isolated electronic states within the diamond band gap, increasing capture efficiency, enabling repeated probing of even a single atom and producing narrow optical linewidths. In this study, we used density functional theory to investigate the formation, structure and electronic properties of crystal defects in diamond containing 229 ⁢Pa, a rare isotope that is predicted to have an exceptionally strong nuclear octupole deformation. In addition, we identified and studied stable lanthanide-containing defects with similar electronic structures as non-radioactive proxies to aid in experimental methods. Our findings hold promise for the existence of such defects and can contribute to the development of a quantum information processing-inspired toolbox of techniques for studying rare isotopes.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2020

Machine learning study of magnetism in uranium-based compounds

Actinide and lanthanide-based materials display exotic properties that originate from the presence of itinerant or localized f electrons and include unconventional superconductivity and magnetism, hidden order, and heavy-fermion behavior. Due to the strongly correlated nature of the 5f electrons, magnetic properties of these compounds depend sensitively on applied magnetic field and pressure, as well as on chemical doping. However, precise connection between the structure and magnetism in actinide-based materials is currently unclear. In this investigation, we established such structure-property links by assembling and mining two datasets that aggregate, respectively, the results of high-throughput density functional theory simulations and experimental measurements for the families of uranium- and neptunium-based binary compounds. Various regression algorithms were utilized to identify correlations among accessible attributes (features or descriptors) of the material systems and predict their cation magnetic moments and general forms of magnetic ordering. Descriptors representing compound structural parameters and cation f-subshell occupation numbers were identified as most important for accurate predictions. The best machine learning model developed employs the random forest regression algorithm. It can predict both spin and orbit moment size with root-mean-square error of 0.17 μ B and 0.19 μ B , respectively. Lastly, the random forest classification algorithm is used to predict the ordering (paramagnetic, ferromagnetic, and antiferromagnetic) of such systems with 76% accuracy.

36 MATERIALS SCIENCE↗
DOE OSTI2020

In Situ Identification of Reaction Intermediates and Mechanistic Understandings of Methane Oxidation over Hematite: A Combined Experimental and Theoretical Study

Effective methane utilization for either clean power generation or value-added chemical production has been a subject of growing attention worldwide for decades, yet challenges persist mostly in relation to methane activation under mild conditions. Here, we report hematite, an earth-abundant material, to be highly effective and thermally stable to catalyze methane combustion at low temperatures (<500 °C) with a low light-off temperature of 230 °C and 100% selectivity to CO 2 . The reported performance is impressive and comparable to those of precious-metal-based catalysts, with a low apparent activation energy of 17.60 kcal·mol –1 . Our theoretical analysis shows that the excellent performance stems from a tetra-iron center with an antiferromagnetically coupled iron dimer on the hematite (110) surface, analogous to that of the methanotroph enzyme methane monooxygenase that activates methane at ambient conditions in nature. Isotopic oxygen tracer experiments support a Mars van Krevelen redox mechanism where CH 4 is activated by reaction with a hematite surface oxygen first, followed by a catalytic cycle through a molecular-dioxygen-assisted pathway. Surface studies with in situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) and density functional theory (DFT) calculations reveal the evolution of reaction intermediates from a methoxy CH 3 –O–Fe, to a bridging bidentate formate b-HCOO–Fe, to a monodentate formate m-HCOO–Fe, before CO 2 is eventually formed via a combination of thermal hydrogen-atom transfer (HAT) and proton-coupled electron transfer (PCET) processes. Finally, the elucidation of the reaction mechanism and the intermediate evolutionary profile may allow future development of catalytic syntheses of oxygenated products from CH 4 in gas-phase heterogeneous catalysis.

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

Non-adiabatic approximations in time-dependent density functional theory: progress and prospects

Time-dependent density functional theory continues to draw a large number of users in a wide range of fields exploring myriad applications involving electronic spectra and dynamics. Although in principle exact, the predictivity of the calculations is limited by the available approximations for the exchange-correlation functional. In particular, it is known that the exact exchange-correlation functional has memory-dependence, but in practise adiabatic approximations are used which ignore this. Here we review the development of non-adiabatic functional approximations, their impact on calculations, and challenges in developing practical and accurate memory-dependent functionals for general purposes.

36 MATERIALS SCIENCE↗
DOE OSTI2025

On-lattice kinetic Monte Carlo approaches for modeling molecular anisotropy in resveratrol crystallization

Stilbenes are a class of organic compounds with broad-ranging pharmaceutical and agricultural applications, which are typically isolated and purified through recrystallization. We are motivated by reducing experimental waste and optimizing yield via developing predictive simulations for processing-dependent crystal morphologies. Using resveratrol as a model stilbene system, we have developed an approach for simulating crystallization with molecular resolution using on-lattice kinetic Monte Carlo. In this work, we highlight modifications to the Stochastic Parallel PARticle Kinetic Simulator (SPPARKS) software package, which were essential to this application. Key enhancements include the incorporation of non-orthogonal cell shapes and monomer anisotropy approximations using bound hard spheres. This new SPPARKS application has been applied to resveratrol with attachment energy libraries obtained from density functional theory, resulting in excellent agreement with experimental morphology prediction.

crystallization↗
DOE OSTI2023

Some Problems in Density Functional Theory

Though calculations based on density functional theory (DFT) are used remarkably widely in chemistry, physics, materials science, and biomolecular research and though the modern form of DFT has been studied for almost 60 years, some mathematical problems remain. From a physical science perspective, it is far from clear whether those problems are of major import. For context, we provide an outline of the basic structure of DFT as it is presented and used conventionally in physical sciences, note some unresolved mathematical difficulties with those conventional demonstrations, then pose several questions regarding both the time-independent and time-dependent forms of DFT that could benefit from attention in applied mathematics. Furthermore, progress on any of these would aid in development of better approximate functionals and in interpretation of DFT.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Constitutive Model of Radiation Aging Effects in Filled Silicone Elastomers under Strain

Filled silicone elastomers, an essential component in many technological applications, are often subjected to controlled or unintended radiation for a variety of reasons. Radiation exposure can lead to permanent mechanical and structural changes in the material, which is manifested as altered mechanical response, and in some cases, a permanent set. For unfilled elastomers, network theories developed and refined over decades can explain these effects in terms of chain-scission and cross-link formation and a hypothesis involving independent networks formed at different strain levels of the material. In this work, we expose a filled silicone rubber to gamma radiation while being under finite elongational strain and show that the observed mechanical and structural changes can be quantitatively modeled within the same theoretical framework developed for unfilled elastomers as long as nuances associated with the Mullins effect are accounted for in a consistent manner. In this work, we employ Ogden’s incompressible hyperelastic model within the framework of Tobolsky’s two-network scheme to describe the observed permanent set and mechanical modulus changes as a function of radiation dosage. In the process, we conclude that gamma radiation induces both direct cross-linking at chain crossings (H-links) and main-chain-scission followed by cross-linking (Y-links). We provide an estimate of the ratio of chain-scission to cross-linking rates, which is in reasonable agreement with previous experimental estimate from Charlesby–Pinner analysis. We use density functional theory (DFT)-based quantum mechanical calculations to explore the stability of –Si and –SiO radicals that form upon a radiation-induced chain-scission event, which sheds light on the relative rates of Y-linking and H-linking processes.

36 MATERIALS SCIENCE↗
DOE OSTI2021

The Rise of Neural Networks for Materials and Chemical Dynamics

Machine learning (ML) is quickly becoming a premier tool for modeling chemical processes and materials. ML-based force fields, trained on large data sets of high-quality electron structure calculations, are particularly attractive due their unique combination of computational efficiency and physical accuracy. This Perspective summarizes some recent advances in the development of neural network-based interatomic potentials. Designing high-quality training data sets is crucial to overall model accuracy. One strategy is active learning, in which new data are automatically collected for atomic configurations that produce large ML uncertainties. Another strategy is to use the highest levels of quantum theory possible. Transfer learning allows training to a data set of mixed fidelity. A model initially trained to a large data set of density functional theory calculations can be significantly improved by retraining to a relatively small data set of expensive coupled cluster theory calculations. These advances are exemplified by applications to molecules and materials.

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

Curated Collection of More than 20,000 Experimentally Reported One-Dimensional Metal–Organic Frameworks

Herein, a collection of more than 20,000 experimentally derived crystal structures for metal–organic frameworks (MOFs) that do not have two- or three-dimensional covalently bonded networks has been developed from the materials available at the Cambridge Crystallographic Data Centre. Of these 20,000 1D MOFs, more than 12,000 structures have been verified to be solvent-free and in exact agreement with the stoichiometry of the synthesized materials. More than 10% of the complete data set comprise materials including two or more distinct metals. The band gaps of more than 12,000 1D MOFs have been computed at the density functional theory-generalized gradient approximation level, finding more than 2000 materials that have a zero band gap. Molecular simulations of CH4 adsorption in a small number of 1D MOFs indicated that adsorbate-induced deformation plays a significant role in determining adsorption isotherms in these materials. As a result, methods that have been used previously for high-throughput predictions of molecular adsorption in 3D MOFs are not suitable for 1D MOFs.

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

The ab initio non-crystalline structure database: empowering machine learning to decode diffusivity

Non-crystalline materials exhibit unique properties that make them suitable for various applications in science and technology, ranging from optical and electronic devices and solid-state batteries to protective coatings. However, data-driven exploration and design of non-crystalline materials is hampered by the absence of a comprehensive database covering a broad chemical space. In this work, we present the largest computed non-crystalline structure database to date, generated from systematic and accurate ab initio molecular dynamics (AIMD) calculations. We also show how the database can be used in simple machine-learning models to connect properties to composition and structure, here specifically targeting ionic conductivity. These models predict the Li-ion diffusivity with speed and accuracy, offering a cost-effective alternative to expensive density functional theory (DFT) calculations. Furthermore, the process of computational quenching non-crystalline structures provides a unique sampling of out-of-equilibrium structures, energies, and force landscape, and we anticipate that the corresponding trajectories will inform future work in universal machine learning potentials, impacting design beyond that of non-crystalline materials. In addition, combining diffusion trajectories from our dataset with models that predict liquidus viscosity and melting temperature could be utilized to develop models for predicting glass-forming ability.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Precisely doping the surface of tin-based electrocatalysts for improved CO 2 conversion to liquid chemicals

Doping tin catalysts with sulfur can improve the electrochemical CO 2 conversion into formate/formic acid, but the lack of composition-dependent activity trends hinders further catalyst development. Here, we precisely controlled the composition of sulfur-doped Sn catalysts to show that sulfur doping only improves CO 2 conversion over a very narrow composition range, achieving maximum activity at 1.4 at% S. In situ Raman spectroscopy indicted working catalysts were in a primarily metallic state (e.g. S-Sn), and we achieved some of the highest reported partial current densities in both H-cell and full-cell electrolyzer configurations. Density Functional Theory calculations predicted S atoms preferentially occupied the catalyst surface and improved CO 2 reduction by localizing charge density at the catalyst/intermediate interface, which stabilized the *OCOH intermediate and lowered the CO 2 conversion thermodynamic barrier. Our work quantifies the composition-dependent influence of S dopants on Sn-based CO 2 reduction catalysts and provides a pathway for maximizing their CO 2 conversion activity.

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

Incorporating the Nanoscale Encapsulation Concept from Liquid Electrolytes into Solid-State Lithium–Sulfur Batteries

Solid-state Li–S batteries are attractive due to their high energy density and safety. However, it is unclear whether the concepts from liquid electrolytes are applicable in the solid state to improve battery performance. Here, we demonstrate that the nanoscale encapsulation concept based on Li 2 S@TiS 2 core–shell particles, originally developed in liquid electrolytes, is effective in solid polymer electrolytes. Using in situ optical cell and sulfur K-edge X-ray absorption, we find that polysulfides form and are well-trapped inside individual particles by the nanoscale TiS 2 encapsulation. We see that this TiS 2 encapsulation layer also functions to catalyze the oxidation reaction of Li 2 S to sulfur, even in solid-state electrolytes, proven by both experiments and density functional theory calculations. A high cell-level specific energy of 427 W·h·kg –1 is achieved by integrating the Li 2 S@TiS 2 cathode with a poly(ethylene oxide)-based electrolyte and a lithium metal anode. This study points to the fruitful direction of borrowing concepts from liquid electrolytes into solid-state batteries.

25 ENERGY STORAGE↗
DOE OSTI2023

Regulating Catalytic Properties and Thermal Stability of Pt and PtCo Intermetallic Fuel-Cell Catalysts via Strong Coupling Effects between Single-Metal Site-Rich Carbon and Pt

Developing low platinum-group-metal (PGM) catalysts for the oxygen reduction reaction (ORR) in proton-exchange membrane fuel cells (PEMFCs) for heavy-duty vehicles (HDVs) remains a great challenge due to the highly demanded power density and long-term durability. Here, this work explores the possible synergistic effect between single Mn site-rich carbon (Mn SA -NC) and Pt nanoparticles, aiming to improve intrinsic activity and stability of PGM catalysts. Density functional theory (DFT) calculations predicted a strong coupling effect between Pt and MnN 4 sites in the carbon support, strengthening their interactions to immobilize Pt nanoparticles during the ORR. The adjacent MnN 4 sites weaken oxygen adsorption at Pt to enhance intrinsic activity. Well-dispersed Pt (2.1 nm) and ordered L1 2 -Pt 3 Co nanoparticles (3.3 nm) were retained on the Mn SA -NC support after indispensable high-temperature annealing up to 800 °C, suggesting enhanced thermal stability. Both PGM catalysts were thoroughly studied in membrane electrode assemblies (MEAs), showing compelling performance and durability. The Pt@Mn SA -NC catalyst achieved a mass activity (MA) of 0.63 A mg Pt –1 at 0.9 V iR-free and maintained 78% of its initial performance after a 30,000-cycle accelerated stress test (AST). The L1 2 -Pt 3 Co@Mn SA -NC catalyst accomplished a much higher MA of 0.91 A mg Pt –1 and a current density of 1.63 A cm –2 at 0.7 V under traditional light-duty vehicle (LDV) H 2 –air conditions (150 kPa abs and 0.10 mg Pt cm –2 ). Furthermore, the same catalyst in an HDV MEA (250 kPa abs and 0.20 mg Pt cm –2 ) delivered 1.75 A cm –2 at 0.7 V, only losing 18% performance after 90,000 cycles of the AST, demonstrating great potential to meet the DOE targets.

25 ENERGY STORAGE↗
DOE OSTI2025

Ir(hkl) Surface Electrochemistry in a Nonadsorbing Acidic Medium

The fundamental properties of electrochemical materials depend on the multiple and often complex interactions between electrode surface sites and electrolyte species at the electrochemical interface. Despite Iridium use in electrolyzer systems, much of its surface electrochemistry remains underexplored. This study investigates the surface electrochemistry of Ir(111), Ir(100), and Ir(110) surfaces in acidic media. Using cyclic voltammetry and CO charge displacement experiments, we establish the charge states and adsorbate coverages as a function of the electrode potential, revealing the presence of hydrogen and hydroxyl co-adsorption at low potentials on (111), and almost no coverage of H ad on (110) facet. In situ Shell Isolated Nanoparticle Enhanced Raman Spectroscopy experiments provide direct evidence of the formation of key adsorbate species, such as hydrogen, hydroxyl, and oxygen, but most importantly, their interactions with interfacial water, confirmed by Density Functional Theory calculations. Our findings highlight the role of co-adsorption and interspecies interactions, with microkinetic adsorption voltammetry simulations corroborating the influence of lateral interactions on adsorption dynamics, particularly for Ir(100) where the OHad formation occurs as a sharp adsorption/desorption current. Our results underscores the importance of interfacial water and hydrogen bonding networks in shaping the electrochemical behavior on Ir surfaces, refining our baseline understanding of the Ir surface electrochemistry necessary for the development of advanced Ir-based electrochemical materials.

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

Efficient diffusion of superdense lithium via atomic channels for dendrite-free lithium–metal batteries

The non-uniform aggregation of fast-diffused Li on an anode surface would aggravate its tip-effect-induced nucleation/growth, leading to the notorious dendrite growth in Li metal batteries (LMBs). Tuning the Li diffusion on the anode surface has been regarded previously as a mainstream method to induce its uniform deposition, while the diffusion of Li in the anode bulk is usually ignored. In this study, conceptually different from the classic surface modification, we propose a molecular tunnelling strategy to construct atomic channels in graphite bulk, which enables the fast diffusion of superdense Li. Density functional theory calculations and ab initio molecular dynamics simulations prove that the bulk diffusion through atomic channels could become a new and dominating path. Its reversible and efficient diffusion has been further visualized by in situ transmission electron microscopy. As a result, when coupled with high-loading LiFePO 4 cathodes (20 mg cm –2 ), a high areal capacity and 100% capacity retention are achieved over 370 cycles. Through this work a new strategy is developed based on the bulk-diffusion of superdense Li for dendrite-free LMBs, which can be pervasive in other high-performance energy storage systems.

25 ENERGY STORAGE↗