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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Laplacian-level meta-generalized gradient approximation for solid and liquid metals

In this work, we derive and motivate a Laplacian-level, orbital-free meta-generalized-gradient approximation (LL-MGGA) for the exchange-correlation energy, targeting accurate ground-state properties of sp and sd metallic condensed matter, in which the density functional for the exchange-correlation energy is only weakly nonlocal due to perfect long-range screening. Our model for the orbital-free kinetic energy density restores the fourth-order gradient expansion for exchange to the r 2 SCAN meta-GGA [Furness et al., J. Phys. Chem. Lett. 11, 8208 (2020)], yielding a LL-MGGA we call OFR2. OFR2 matches the accuracy of SCAN for prediction of common lattice constants and improves the equilibrium properties of alkali metals, transition metals, and intermetallics that were degraded relative to the PBE GGA values by both SCAN and r 2 SCAN. We compare OFR2 to the r 2 SCAN -L LL-MGGA [Mejia-Rodriguez and Trickey, Phys. Rev. B 102, 121109(R) (2020)] and show that OFR2 tends to outperform r 2 SCAN -L for the equilibrium properties of solids, but r 2 SCAN -L much better describes the atomization energies of molecules than OFR2 does. For best accuracy in molecules and nonmetallic condensed matter, we continue to recommend SCAN and r 2 SCAN. Numerical performance is discussed in detail, and our paper provides an outlook to machine learning.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Insights into Cation Ordering of Double Perovskite Oxides from Machine Learning and Causal Relations

This work investigates origins of cation ordering in double perovskites using first-principles theory computations combined with machine learning (ML) and causal relations. We have considered various oxidation states of A, A', B, and B' from the family of transition metal ions to construct a diverse compositional space. A conventional framework employing traditional ML classification algorithms such as Random Forest (RF) coupled with appropriate features including geometry-driven and key structural modes leads to accurate prediction (~98%) of A-site cation ordering. We have evaluated the accuracy of ML models by employing analyses of decision paths, assignments of probabilistic confidence bound, and finally a direct non-Gaussian acyclic structural equation model to investigate causality. Our study suggests that structural modes are crucial for classifying layered, columnar, and rock-salt ordering. The charge difference between A and A' is the most important feature for predicting clear layered ordering, which in turn depends on the B and B' charge separation. We have also designed mathematical relationships with these features to derive energy differences to form clear layered ordering. Here, the trilinear coupling between tilt, in-phase rotation, and A-site antiferroelectric displacement in the Landau free-energy expansion becomes the necessary condition behind formation of A-site cation ordering.

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

Capturing Surface Coverage Effects in Heterogeneous Catalysis

Adsorbate–adsorbate lateral interactions at relevant surface coverages have a significant effect on chemical kinetics, thereby influencing the activity of a heterogeneous catalyst. Coverage-dependent kinetic and thermodynamic parameters therefore must be included in studies of such complex systems to properly predict the turnover frequencies and kinetic trends. Thus, it becomes extremely important to accurately capture the strength of lateral interactions between neighboring species under realistic reaction conditions. In this Perspective, we discuss the various existing computational and experimental methods for determining adspecies coverage and configurational effects. The choice of the tools and methods employed in such studies depends on factors such as time, length scales, computational cost, the presence of solvents, and reaction conditions. The applications of each method and the respective challenges are also discussed here. As a result, we discuss the recent developments and future of the state-of-the-art for inclusion of surface coverage and configuration into a holistic picture for accurate predictions of catalytic behavior.

09 BIOMASS FUELS↗
DOE OSTI2020

Surface Amorphization of Vanadium Dioxide (B) for K-ion Battery

Given the merits of low cost, fast ionic transport in electrolyte, and high operating voltage, potassium ion batteries (PIBs) are promising alternatives to lithium-ion batteries. However, developing suitable electrode materials that can reversibly accommodate large potassium ions is a great challenge. In this paper, guided by density functional theory (DFT) calculations, it is demonstrated that the strategy of interfacial engineering via surface amorphization of VO 2 (B) nanorods (SA-VO 2 ), which results in the formation of a crystalline core/amorphous shell heterostructure, enables superior K + storage performance in terms of large capacity, outstanding rate capability, and long cycle stability working as an anode for PIBs. DFT calculations reveal that the created crystalline/amorphous heterointerface in SA-VO 2 can substantially lower the surface energy, narrow the band gap, and reduce the K + diffusion barrier of VO 2 (B). These conditions enable enhanced K + storage capacity and rapid K + /electron transfer, which result in large capacity and outstanding rate capability. Using in situ X-ray diffraction and in situ transmission electron microscopy complemented by ex situ microscopic and spectroscopic techniques, it is unveiled that the superior cycling stability originates from the excellent phase reversibility with negligible strain response and robust mechanical behavior of SA-VO 2 upon (de)potassiation.

25 ENERGY STORAGE↗
DOE OSTI2022

Conformal LiF Stabilized Interfaces via Electrochemical Fluorination on High Voltage Spinel Cathodes (≈4.9 V) for Lithium-Ion Batteries

The high voltage LiNi 0.5 Mn 1.5 O 4 (LNMO) spinel is one of the promising cathodes for the lithium-ion batteries due to its high energy densities, good rate performance. However, its high operating potential (≈4.75 V) causes extensive oxidation of conventional carbonate electrolytes, resulting an unstable and thick cathode electrolyte interphase (CEI) layer with a large irreversible capacity and low coulombic efficiency. In this work, we report the formation of thin LiF stabilized interfaces on LNMO via electrochemical fluorination that significantly improves the cycling stability and enhanced the capacity. An electrochemically induced conformal LiF layer acts as a part of a robust CEI by reducing the leakage of electrons and allowing the conduction of Li ions through it. Because of the robust LiF stabilized CEI, LNMO delivers a discharge capacity of ≈148.5 and ≈117.1 mAh g -1 at 0.1 and 1 C rate, respectively. It exhibits excellent cyclability with 80% capacity retention (CR) after 600 cycles in lithium-half cell and ≈90% CR after 200 cycles in full cell with only 0.03% and 0.05% capacity decay per cycle in conventional carbonate electrolytes without additives. Such an excellent electrochemical performance could lead to the potential development of high energy density batteries with high voltage cathodes for grid-based applications.

25 ENERGY STORAGE↗
DOE OSTI2024

Degradation of performance in ICF implosions due to Rayleigh–Taylor instabilities: A Hamiltonian perspective

The Rayleigh–Taylor instability (RTI) is an ubiquitous phenomenon that occurs in inertial-confinement-fusion (ICF) implosions and is recognized as an important limiting factor of ICF performance. To analytically understand the RTI dynamics and its impact on ICF capsule implosions, we develop a first-principle variational theory that describes an imploding spherical shell undergoing RTI. The model is based on a thin-shell approximation and includes the dynamical coupling between the imploding spherical shell and an adiabatically compressed fluid within its interior. Using a quasilinear analysis, we study the degradation trends of key ICF performance metrics (e.g., stagnation pressure, residual kinetic energy, and areal density) as functions of initial RTI parameters (e.g., the initial amplitude and Legendre mode), as well as the 1D implosion characteristics (e.g., the convergence ratio). We compare analytical results from the theory against nonlinear results obtained by numerically integrating the governing equations of this reduced model. Our findings emphasize the need to incorporate polar flows in the calculation of residual kinetic energy and demonstrate that higher convergence ratios in ICF implosions lead to significantly greater degradation of key performance metrics.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Elucidation of complex triplet excited state dynamics in Pd(ii) biladiene tetrapyrroles

Pd(II) biladienes have been developed over the last five years as non-aromatic oligotetrapyrrole complexes that support a rich triplet photochemistry. In this work, we have undertaken the first detailed photophysical interrogation of three homologous Pd(II) biladienes bearing different combinations of methyl- and phenyl-substituents on the frameworks’ sp 3 -hybridized meso-carbon (i.e., the 10-position of the biladiene framework). These experiments have revealed unexpected excited-state dynamics that are dependent on the wavelength of light used to excite the biladiene. More specifically, transient absorption spectroscopy revealed that higher-energy excitation (λ exc ~ 350–500 nm) led to an additional lifetime (i.e., an extra photophysical process) compared to experiments carried out following excitation into the lowest-energy excited states (λ exc = 550 nm). Each Pd(II) biladiene complex displayed an intersystem crossing lifetime on the order of tens of ps and a triplet lifetime of ~20 μs, regardless of the excitation wavelength. However, when higher-energy light is used to excite the complexes, a new lifetime on the order of hundreds of ps is observed. The origin of the ‘extra’ lifetime observed upon higher energy excitation was revealed using density functional theory (DFT) and time-dependent DFT (TDDFT). These efforts demonstrated that excitation into higher-energy metal-mixed-charge-transfer excited states with high spin–orbit coupling to higher energy metal-mixed-charge-transfer triplet states leads to the additional excitation deactivation pathway. Furthermore, the results of this work demonstrate that Pd(II) biladienes support a unique triplet photochemistry that may be exploited for development of new photochemical schemes and applications.

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

Accurate Prediction of Adiabatic Ionization Potentials of Organic Molecules using Quantum Chemistry Assisted Machine Learning

In previous work (Dandu et al., J. Phys. Chem. A, 2022, 126, 4528–4536), we were successful in predicting accurate atomization energies of organic molecules using machine learning (ML) models, obtaining an accuracy as low as 0.1 kcal/mol compared to the G4MP2 method. In this work, we extend the use of these ML models to adiabatic ionization potentials on data sets of energies generated using quantum chemical calculations. Atomic specific corrections that were found to improve atomization energies from quantum chemical calculations have also been used in this study to improve ionization potentials. Here, the quantum chemical calculations were performed on 3405 molecules containing eight or fewer non-hydrogen atoms derived from the QM9 data set, using the B3LYP functional with the 6–31G(2df,p) basis set for optimization. Low-fidelity IPs for these structures were obtained using two density functional methods: B3LYP/6–31+G(2df,p) and ωB97XD/6–311+G(3df,2p). Highly accurate G4MP2 calculations were performed on these optimized structures to obtain high-fidelity IPs to use in ML models based on the low-fidelity IPs. Our best performing ML methods gave IPs of organic molecules within a mean absolute deviation of 0.035 eV from the G4MP2 IPs for the whole data set. This work demonstrates that ML predictions assisted by quantum chemical calculations can be used to successfully predict IPs of organic molecules for use in high throughput screening.

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

How Accurate Are Approximate Density Functionals for Noncovalent Interaction of Very Large Molecular Systems?

Noncovalent intermolecular interactions are very important in many research areas. Therefore, it is vital to understand the extent to which approximate density functionals give a proper description of noncovalent interactions. Previous research has demonstrated that some approximate density functionals can predict usefully accurate interaction energies for many noncovalent systems; however, most of that work is limited to small and moderate-sized molecules. Very recently though, accurate benchmarks have become available for some very large molecules. Here, the present work applies 21 approximate density functionals to compute the binding energies of seven large molecular systems that have a number of atoms ranging from 200 to 910. The results are judged by comparison to the recently published CIM-DLPNO-CCSD(T) results, which are assumed to provide a reliable benchmark. The five most accurate methods among those tested are found to be PW6B95-D4, PW6B95-D3(BJ), revM11, M06-L, and MN15.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Ripening of Rh Nanoparticle Catalysts in Reverse Water–Gas Shift via a Data-Driven Model Combining Physics, Theory, and Experiment

Degradation via sintering is an ongoing challenge that impedes the broad commercial success of supported metallic nanoparticle catalysts. To mitigate degradation via informed catalyst design and process operations, here we aim to disambiguate the underlying mechanisms of sintering by combining theory and experiment in a quantitative framework. While mechanistic sintering models exist, they only model a single sintering pathway, even though multiple sintering mechanisms can occur simultaneously or dominate at different stages of the process. Data-driven machine learning models have emerged as a means to represent complex processes through data regression. However, machine learning models have very large data needs and lack mechanistic insights due to their black-box encoding. To develop an interpretive model of catalyst degradation via sintering, we constructed a hybrid model combining mechanistic “physics-based” models and data-driven methods to obtain both reliable predictions and mechanistic insights regarding experimentally observed sintering phenomena. Focusing on nanoparticle sintering in the Rh–TiO 2 catalyst for the reverse water–gas shift (RWGS) reaction, the hybrid model couples a mechanistic term for Ostwald ripening with energy values calculated via density functional theory (DFT) with a parametric, data-driven discrepancy function term for unmodeled mechanisms. The hybrid model is trained using Bayesian inference with data collected from small-angle X-ray scattering (SAXS) in situ experiments wherein average nanoparticle diameter versus time was measured at three relevant operating temperatures. The calibrated hybrid model results show that an Ostwald ripening-only model parameterized with fixed DFT energies does not fully capture the time and temperature dependence of the SAXS-observed sintering kinetics, and that an additional functional contribution, or DFT energy calibration, is required to reconcile simulation and experiment. Analysis of the hybrid-model error confirms that the hybrid model outperforms both the purely mechanistic and purely data-driven alternatives in terms of expected predictive accuracy for time-evolving average particle sizes. Furthermore, the results support the hypothesis that the Ostwald ripening mechanism is less important for explaining the sintering phenomena as operating temperature increases under an assumed fixed DFT parameterization. This could be explained in one of two ways: either latent, unmodeled sintering mechanisms dominate at higher temperatures, or the DFT uncertainty increases with temperature. The proposed modeling approach directly links theory to experiments and simulations via a statistical hybrid modeling framework and can be extended to other catalytic systems to improve predictive models and mechanistic understanding.

Bayesian hybrid modeling↗
DOE OSTI2020

Understanding electronic peculiarities in tetragonal FeSe as local structural symmetry breaking

Traditional band theory of perfect crystalline solids often uses as input the structure deduced from diffraction experiments; when modeled by the minimal unit cell this often produces a spatially averaged model. The present study illustrates that this is not always a safe practice unless one examines if the intrinsic bonding mechanism is capable of benefiting from the formation of a distribution of lower symmetry local environments that differ from the macroscopically averaged structure. This can happen either due to positional or to magnetic symmetry breaking. By removing the constraint of a small crystallographic cell, the energy minimization in the density functional theory finds atomic and spin symmetry breaking, not evident in conventional diffraction experiments but being found by local probes such as atomic pair distribution function analysis. Here in this paper we report that large atomic and electronic anomalies in bulk tetragonal FeSe emerge from the existence of distributions of local positional and magnetic moment motifs. The found symmetry-broken motifs obtained by minimization of the internal energy represent what chemical bonding in the tetragonal phase prefers as intrinsic energy lowering (stabilizing) static distortions. This explains observations of band renormalization, predicts orbital order and enhanced nematicity, and provides unprecedented close agreement with spectral function measured by photoemission and local atomic environment revealed by the pair distribution function. While the symmetry-restricted strong correlation approach has been argued previously to be the exclusive theory needed for describing the main peculiarities of FeSe, we show here that the symmetry-broken mean-field approach addresses numerous aspects of the problem, provides intuitive insight into the electronic structure, and opens the door for large-scale mean-field calculations for similar d -electron quantum materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Probability Density Function Control of Frequency Fluctuations in Renewable-Rich Power Systems

The stochastic nature of renewable energy sources (RESs) necessitates treating power system frequency response as a random process with a nonstationary probability density function (PDF). Here, based upon the stochastic distribution control theory originated by the second author, this paper proposes a novel stochastic controller to improve the frequency PDF in power grids when integrating a large amount of RESs, thereby minimizing the effects of uncertainties and enhancing overall system stability. The key idea is to manipulate the controllable power generation resources so that the frequency PDF is make to follow a target PDF by using the stochastic distribution control theory originated by the second author. The proposed method can easily be plugged into existing automatic generation controls for multi-area transmission grids. The proposed method is validated via a modified Kundar's two area system and 240-bus Western Electricity Coordinating Council systems. The simulation results show that the proposed control shapes the frequency PDF narrower and sharper, leading to a notable improvement toward minimizing the effects of randomness and uncertainty during grid operation.

frequency↗
DOE OSTI2021

Mechanisms of water oxidation on heterogeneous catalyst surfaces

Water oxidation, an essential step in photosynthesis, has attracted intense research attention. Understanding the reaction pathways at the electrocatalyst/water interface is of great importance for the development of water oxidation catalysts. How the water is oxidized on the electrocatalyst surface by the positive charges is still an open question. This review summarizes current advances in studies on surface chemistry within the context of water oxidation, including the intermediates, reaction mechanisms, and their influences on the reaction kinetics. The Tafel analyses of some electrocatalysts and the rate-laws relative to charge consumption rates are also presented. Moreover, how the multiple charge transfer relies on the intermediate coverage and the accumulated charge numbers is outlined. Finally, the intermediates and rate-determining steps on some water oxidation catalysts are discussed based on density functional theories.

14 SOLAR ENERGY↗
DOE OSTI2025

Quantum Emitters Induced by High Pressure and UV Laser Irradiation in Multilayer GaSe

In this work, we report on defect generation in multilayer GaSe through hydrostatic pressure quenching and UV laser irradiation. The Raman line width from the UV 266 nm irradiated sample is much wider than that in pressure-quenched GaSe, corresponding to a wider defect energy distribution range in the former sample than the latter. After quenching from 11.2 GPa, three photoluminescence (PL) peaks from defect states are observed at 657, 681, and 695 nm at a low temperature of 93 K. Defect-related peaks at 649, 694, 750, and 774 nm also appear in low-temperature PL spectra after UV laser irradiation, with a nonmonotonous intensity dependence on irradiation duration. There are common features in defects produced by these two methods: the PL peaks with the lowest energy are sharp, and their PL intensities increase linearly with the excitation laser power and saturate above a certain excitation laser power. These two features are similar to those in defects for single-photon emission (SPE) in other 2D materials at even lower temperatures. Fluorescence lifetime imaging shows distinguished short (2.3 ns) and long (75.6 nm) lifetimes of the 695 nm PL line in pressure-quenched GaSe. The density functional theory predicts defect energy levels related to Se vacancy.

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

Ni Nanoparticles on CeO 2 (111): Energetics, Electron Transfer, and Structure by Ni Adsorption Calorimetry, Spectroscopies, and Density Functional Theory

The morphology, interfacial bonding energetics and charge transfer of Ni clusters and nanoparticles on slightly-reduced CeO 2-x (111) surfaces at 100 to 300 K have been studied using single crystal adsorption calorimetry (SCAC), low-energy ion scattering spectroscopy (LEIS), Xray photoelectron spectroscopy (XPS), low energy electron diffraction (LEED) and density functional theory (DFT). The initial heat of adsorption of Ni vapor decreased with the extent of pre-reduction (x) of the CeO 2-x (111), showing that stoichiometric ceria adsorbs Ni more strongly than oxygen vacancies. On CeO 1.95 (111) at 300 K, the heat dropped quickly with coverage in the first 0.1 ML, attributed to nucleation of Ni clusters on stoichiometric steps, followed by the Ni particles spreading onto less favorable terrace sites. At 100 K, the clusters nucleate on terraces due to slower diffusion. Adsorbed Ni monomers are in the +2 oxidation state, and they bind by ~45 kJ/mol more strongly to step sites than terraces. The measured heat of adsorption versus average particle size on terraces is favorably compared to DFT calculations. The Ce 3d XPS lineshape showed an increase in Ce 3+ /Ce 4+ ratio with Ni coverage, providing the number of electrons donated to the ceria per Ni atom. The charge transferred per Ni is initially large but strongly decreases with increasing cluster size for both experiments and DFT, and shows large differences between clusters at steps versus terraces. This charge is localized on the interfacial Ni and Ce atoms in their atomic layers closest to the interface. This knowledge is crucial to understanding the nature of the active sites on the surface of Ni-CeO 2 catalysts for which metal-oxide interactions play a very important role in the activation of O-H and C-H bonds. In conclusion, the changes in these interactions with Ni particle size (metal loading) and the extent of reduction of the ceria help to explain how previously reported catalytic activity and selectivity change with these same structural details.

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

Understanding Fragmentation of Organic Small Molecules in Atom Probe Tomography

In atom probe tomography of molecular organic materials, field ionization of either entire molecules or molecular fragments can occur, but the mechanism governing this behavior was not previously understood. This work explains when a doubly ionized small molecule organic material is expected to undergo fragmentation. Here, we find that multiple detection events arising from post-ionization fragmentation of a parent molecular dication into two daughter ions is well explained by the free energy and geometries of the molecules computed using density functional theory. Of the systems studied, exergonic free energies for formation of the daughter ions, smaller activation energies for dissociation, and increases in bond length are all found to be quantitative predictors for ion fragmentation. This work expands the applicability of atom probe tomography to organic materials by increasing the fundamental understanding of processes occurring during this analysis technique.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Efficient Parameterization of Density Functional Tight-Binding for 5 f -Elements: A Th–O Case Study

Density functional tight binding (DFTB) models for f-element species are challenging to parametrize owing to the large number of adjustable parameters. The explicit optimization of the terms entering the semiempirical DFTB Hamiltonian related to f orbitals is crucial to generating a reliable parametrization for f-block elements, because they play import roles in bonding interactions. However, since the number of parameters grows quadratically with the number of orbitals, the computational cost for parameter optimization is much more expensive for the f-elements than for the main group elements. In this work we present a set of efficient approaches for mitigating the hurdle imposed by the large size of the parameter space. A novel group-by-orbital correction functions for two-center bond integrals was developed. With this approach the number of parameters is reduced, and it grows linearly with the number of elements, maintaining the accuracy and the number of parameters, in the case of f elements, by more than 40%. The parameter optimization step was accelerated by means of the mini-batch BFGS method. This method allows parameter optimizations with much larger training sets than other single batch methods. A stochastic optimizer was employed that helped overcome shallow local minima in the objective function. The proposed algorithm was used to parametrize the DFTB Hamiltonian for the Th–O system, which was subsequently applied to the study of ThO 2 nanoparticles. The training set consisted of 6322 unique structures, which is barely feasible with conventional optimization methods. The optimized parameter set, LANL-ThO, displays good agreement with DFT-calculated properties such as energies, forces, and structures for both clusters and bulk ThO 2 . Benefiting from the fewer number of parameters and lower computational costs for objective function evaluations, this new approach shows its potential applications in DFTB parametrization for elements with high angular momentum, which present a challenge to conventional methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗