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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Density Functional Theory Solute-Dislocation Interactions

DFT-optimized structures of dislocations with a solute at different sites. The dataset include the VASP input files (except POTCAR) and output files OSZICAR and OUTCAR. There are also text files containing the DFT-computed interaction energies. The README file contains expressions that can be used to compute the interaction energies and chemical misfit energies using the volumetric size misfit for any solute species in Mg.

Trinkle, Dallas↗
DOE OSTI2021

Density Functional Theory Solute-Dislocation Interactions

DFT-optimized structures of dislocations with a solute at different sites. The dataset include the VASP input files (except POTCAR) and output files OSZICAR and OUTCAR. There are also text files containing the DFT-computed interaction energies. The README file contains expressions that can be used to compute the interaction energies and chemical misfit energies using the volumetric size misfit for any solute species in Mg.

Trinkle, Dallas↗
DOE OSTI2021

Density Functional Theory Solute-Dislocation Interactions

DFT-optimized structures of dislocations with a solute at different sites. The dataset include the VASP input files (except POTCAR) and output files OSZICAR and OUTCAR. There are also text files containing the DFT-computed interaction energies. The README file contains expressions that can be used to compute the interaction energies and chemical misfit energies using the volumetric size misfit for any solute species in Mg.

Trinkle, Dallas↗
DOE OSTI2024

Analytical ab initio hessian from a deep learning potential for transition state optimization

Identifying transition states—saddle points on the potential energy surface connecting reactant and product minima—is central to predicting kinetic barriers and understanding chemical reaction mechanisms. In this work, we train a fully differentiable equivariant neural network potential, NewtonNet, on thousands of organic reactions and derive the analytical Hessians. By reducing the computational cost by several orders of magnitude relative to the density functional theory (DFT) ab initio source, we can afford to use the learned Hessians at every step for the saddle point optimizations. We show that the full machine learned (ML) Hessian robustly finds the transition states of 240 unseen organic reactions, even when the quality of the initial guess structures are degraded, while reducing the number of optimization steps to convergence by 2–3× compared to the quasi-Newton DFT and ML methods. All data generation, NewtonNet model, and ML transition state finding methods are available in an automated workflow.

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

On the functional dependence of transition-potential coupled cluster

Orbital relaxation of the core region is a primary source of error in the computation of core ionization and core excitation energies. Recently, Transition-Potential Coupled Cluster (TP-CC) methods have been used to explicitly treat orbital relaxation using non-variational molecular orbitals determined by reoccupation of orbitals optimized for a fractional core occupation. The amount of fractional occupation is governed by parameter λ, and recommended values for accurate TP-CCSD and XTP-CCSD computations of carbon, nitrogen, oxygen, and fluorine K edges were previously determined. Herein, we explore the performance of several density functionals for generating the fractionally occupied orbitals used in TP-CCSD. These functionals include HF, BP86, BH&HLYP, B3LYP, M06-2X, and ωB97m-V. The fractionally occupied orbitals computed across the various functionals were subsequently employed as the initial orbitals for our TP-CCSD calculations of organic K-edge x-ray absorption and photoelectron spectra. Regardless of the functional used to generate the fractionally occupied orbitals, the TP-CCSD calculations yield accurate and comparable core ionization energies, core excitation energies, and oscillator strengths.

Coupled-cluster methods↗
DOE OSTI2025

HFBTHO-AD: Differentiation of a nuclear energy density functional code

The HFBTHO code implements a nuclear energy density functional solver to model the structure of atomic nuclei. HFBTHO has previously been used to calibrate energy functionals and perform sensitivity analysis by using derivative-free methods. To enable derivative-based optimization and uncertainty quantification approaches, we must compute the derivatives of HFBTHO outputs with respect to the parameters of the energy functional, which are a subset of all input parameters of the code. Here, we use the algorithmic/automatic differentiation (AD) tool Tapenade to differentiate HFBTHO. We compare the derivatives obtained using AD against finite-difference approximation and examine the performance of the derivative computation.

Algorithmic differentiation↗
DOE OSTI2021

Optimized structure and electronic band gap of monolayer GeSe from quantum Monte Carlo methods

Here, we have used highly accurate quantum Monte Carlo methods to determine the chemical structure and electronic band gaps of monolayer GeSe. Two-dimensional (2D) monolayer GeSe has received a great deal of attention due to its unique thermoelectric, electronic, and optoelectronic properties with a wide range of potential applications. Density functional theory (DFT) methods have usually been applied to obtain optical and structural properties of bulk and 2D GeSe. For the monolayer, DFT typically yields a larger band-gap energy than for bulk GeSe but cannot conclusively determine if the monolayer has a direct or indirect gap. Moreover, the DFT-optimized lattice parameters and atomic coordinates for monolayer GeSe depend strongly on the choice of approximation for the exchange-correlation functional, which makes the ideal structure-and its electronic properties-unclear. In order to obtain accurate lattice parameters and atomic coordinates for the monolayer, we use a surrogate Hessian-based parallel line search within diffusion Monte Carlo to fully optimize the GeSe monolayer structure. The DMC-optimized structure is different from those obtained using DFT, as are calculated band gaps. The potential energy surface has a shallow minimum at the optimal structure. This, combined with the sensitivity of the electronic structure to strain, suggests that the optical properties of monolayer GeSe are highly tunable by strain.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Polynomial Scaling Localized Active Space Unitary Selective Coupled Cluster Singles and Doubles

We present a polynomial-scaling algorithm for the localized active space unitary selective coupled cluster singles and doubles (LAS-USCCSD) method. In this approach, cluster excitations are selected based on a threshold ϵ determined by the absolute gradients of the LAS-UCCSD energy with respect to cluster amplitudes. Using the generalized Wick’s theorem for multireference wave functions, we derive the gradient expression as a polynomial function of one-, two-, and three-body reduced density matrices and 1- and 2-electron integrals, valid for any multireference wave function. The resulting gradient implementation exhibits a memory scaling of 𝒪(N 6 ), with N spin orbitals in the combined active space of all fragments. The variational quantum eigensolver is used to optimize the selected cluster excitations on a quantum simulator. Furthermore, by plotting the energy error, defined as the difference between the LAS-USCCSD and corresponding CASCI energies, against the inverse cluster amplitude selection threshold (ϵ –1 ) for polyene chains containing 2 to 5 π-bond units, we establish a relationship between the energy error and the threshold. To further validate the accuracy of LAS-USCCSD, we computed the cis–trans isomerization energy of stilbene (a 20-qubit system) and the magnetic coupling constant of the tris-hydroxo-bridged chromium dimer [Cr 2 (OH) 3 (NH 3 ) 6 ] 3+ (evaluated as both 12- and 20-qubit systems) using the Qiskit-Qulacs simulator. Assessing such examples is important to determine the practical feasibility of quantum simulations for chemically realistic systems. Toward this goal, with the LAS-USCCSD algorithm we estimated the quantum resources required for simulating an active space of (30e,22o) in [Cr 2 (OH) 3 (NH 3 ) 6 ] 3+ , a size that remains beyond the reach of current quantum simulators for accurate treatment.

Algorithms↗
DOE OSTI2020

Engineering the electronic and strained interface for high activity of PdM core @Pt monolayer electrocatalysts for oxygen reduction reaction

Alloyed nanoparticles with core-shell structures provide a favorable model to modulate interfacial interaction and surface structures at the atomic level, which is important for designing electrocatalysts with high activity and durability. Herein, core-shell structured Pd 3 M@Pt/C nanoparticles with binary PdM alloy cores (M = Fe, Ni, and Co) and a monolayer Pt shell were successfully synthesized with diverse interfaces. Among these, Pd 3 Fe@Pt/C exhibited the best oxygen reduction reaction catalytic performance, roughly 5.4 times more than that of the commercial Pt/C catalyst used as reference. The significantly enhanced activity is attributed to the combined effects of strain engineering, interfacial electron transfer, and improved Pt utilization. Density functional theory simulations and extended X-ray absorption fine structure analysis revealed that engineering the alloy core with moderate lattice mismatch and alloy composition (Pd 3 Fe) optimizes the surface oxygen adsorption energy, thereby rendering excellent electrocatalytic activity. Finally, future researches may use this study as a guide on the construction of highly effective core-shell electrocatalysts for various energy conversions and other applications.

25 ENERGY STORAGE↗
DOE OSTI2026

Identification of In Situ Generated High-Spin Fe(II)-N 4 Active Sites for Acidic Oxygen Reduction Reaction via Operando 57 Fe Mössbauer Spectroscopy

Iron-nitrogen-carbon (FeNC) catalysts are considered among the most promising alternative to Pt catalysts in acidic oxygen reduction reaction (ORR), yet the geometric and electronic structures of the true active site under reaction conditions have not been clearly elucidated. Herein, we synthesized a representative FeNC catalyst by pyrolyzing Fe 3+ absorbed on ZIF-8-derived N-doped carbon at a mild temperature under the H 2 /Ar atmosphere, where a formation mechanism of FeN 4 sites through a Zn-mediated Fe nanoparticle atomization process was proposed. The resulting FeNC-750 catalyst shows high acidic ORR activity with a half-wave potential of 0.838 V and a peak power density of 1 W cm -2 in proton-exchange membrane fuel cell (PEMFC). By using operando 57 Fe Mössbauer spectroscopy on FeNC-750, it was revealed that the pyrrolic N-coordinated high-spin Fe 2+ N 4 sites, which are in situ generated from high-spin Fe 3+ N 4 during ORR, are identified as catalytic active states. Density functional theory calculations further verified that, compared to the pyridinic N-coordinated low-spin Fe 2+ N 4 , the pyrrolic N-coordinated high-spin Fe 2+ exhibits optimized adsorption energy for reaction intermediates, thereby lowering the energy barrier of the rate-determining step (RDS) and facilitating OH* desorption. In conclusion, this work provides both experimental and theoretical evidence of the true active site in the FeNC during acidic ORR, offering significant insights for the rational design of high-performance materials for acidic fuel cells.

FeNC catalyst↗
DOE OSTI2023

Prediction of the structures and heats of formation of MO 2 , MO 3 , and M 2 O 5 for M = V, Nb, Ta, Pa

Structures for the mono-, di-, and tri-bridge isomers of M 2 O 5 as well as those for the MO 2 and MO 3 fragments for M = V, Nb, Ta, and Pa were optimized at the density functional theory (DFT) level. Single point CCSD(T) calculations extrapolated to the complete basis set (CBS) limit at the DFT geometries were used to predict the energetics. Here, the lowest energy dimer isomer was the di-bridge for M = V and Nb and the tri-bridge for M = Ta and Pa. The di-bridge isomers were predicted to be composed of MO 2 + and MO 3 - fragments, whereas the mono- and tri-bridge are two MO 2 + fragments linked by an O 2 - . The heats of formation of M 2 O 5 dimers, as well as MO 2 and MO 3 neutral and ionic species were predicted using the Feller–Peterson–Dixon (FPD) approach. The heats of formation of the MF 5 species were calculated to provide additional benchmarks. Dimerization energies to form the M 2 O 5 dimers are predicted to become more negative going down group 5 and range from -29 to -45 kcal mol -1 . The ionization energies (IEs) for VO 2 and TaO 2 are essentially the same at 8.75 eV whereas the IEs for NbO 2 and PaO 2 are 8.10 and 6.25 eV, respectively. The predicted adiabatic electron affinities (AEAs) range from 3.75 eV to 4.45 eV for the MO 3 species and vertical detachment energies from 4.21 to 4.59 eV for MO 3 - . The calculated M[double bond, length as m-dash]O bond dissociation energies increase from 143 kcal mol -1 for M = V to ~170 kcal mol -1 for M = Nb and Ta to ~200 kcal mol -1 for M = Pa. The M–O bond dissociation energies are all similar ranging from 97 to 107 kcal mol -1 . Natural bond analysis provided insights into the types of chemical bonds in terms of their ionic character. Pa 2 O 5 is predicted to behave like an actinyl species dominated by the interactions of approximately linear PaO 2 + groups.

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

High entropy powering green energy: hydrogen, batteries, electronics, and catalysis

A reformation in energy is underway to replace fossil fuels with renewable sources, driven by the development of new, robust, and multi-functional materials. High-entropy materials (HEMs) have emerged as promising candidates for various green energy applications, having unusual chemistries that give rise to remarkable functionalities. This review examines recent innovations in HEMs, focusing on hydrogen generation/storage, fuel cells, batteries, semiconductors/electronics, and catalysis—where HEMs have demonstrated the ability to outperform state-of-the-art materials. We present new master plots that illustrate the superior performance of HEMs compared to conventional systems for hydrogen generation/storage and heat-to-electricity conversion. We highlight the role of computational methods, such as density functional theory and machine learning, in accelerating the discovery and optimization of HEMs. The review also presents current challenges and proposes future directions for the field. We emphasize the need for continued integration of modeling, data, and experiments to investigate and leverage the underlying mechanisms of the HEMs that are powering progress in sustainable energy.

batteries↗
DOE OSTI2024

Factors influencing quantum evaporation of helium from polar semiconductors from first principles

While there is much indirect evidence for the existence of dark matter (DM), to date it has evaded detection. Current efforts focus on DM masses over ~GeV—to push the sensitivity of DM searches to lower masses, new DM targets and detection schemes are needed. In this work, we focus on the latter—a novel detection scheme recently proposed to detect 10–100 meV phonons in polar target materials. Previous work showed that well-motivated models of DM can interact with polar semiconductors to produce an athermal population of phonons. This new sensing scheme proposes that these phonons then facilitate quantum evaporation of 3 He from a van der Waals film deposited on the target material. However, a fundamental understanding of the underlying process is still unclear, with several uncertainties related to the precise rate of evaporation and how it can be controlled. In this work, we use ab initio density functional theory calculations to compare the adsorption energies of helium atoms on a polar target material, sodium iodide, to understand the underlying evaporation physics. We explore the role of surface termination, monolayer coverage, and elemental species on the rate of He evaporation from the target material. Using this, we discuss the optimal target features for He-evaporation experiments and their range of tunability through chemical and physical modifications such as applied field and surface termination.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Comparing the Expense and Accuracy of Methods to Simulate Atomic Vibrations in Rubrene

Atomic vibrations can inform about materials properties from hole transport in organic semiconductors to correlated disorder in metal–organic frameworks. Currently, there are several methods for predicting these vibrations using simulations, but the accuracy–efficiency tradeoffs have not been examined in depth. In this work, rubrene is used as a model system to predict atomic vibrational properties using six different simulation methods: density functional theory, density functional tight binding, density functional tight binding with a Chebyshev polynomial-based correction, a trained machine learning model, a pretrained machine learning model called ANI-1, and a classical forcefield model. The accuracy of each method is evaluated by comparison to the experimental inelastic neutron scattering spectrum. All methods discussed here show some accuracy across a wide energy region, though the Chebyshev-corrected tight-binding method showed the optimal combination of high accuracy with low expense. We then offer broad simulation guidelines to yield efficient, accurate results for inelastic neutron scattering spectrum prediction.

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

Binding of uranyl cations to a Zr-based metal-organic framework by density functional theory

Zr-based metal–organic frameworks (Zr-MOFs) have been widely used as ion adsorbents for the removal or extraction of toxic and/or radionuclide species from aqueous solutions. However, the mechanisms by which uranyl cations (UO 2 2+ ) interact with Zr-MOFs have not been established. In this work, the nature of the bonding of uranyl cations with a Zr-MOF was determined using density functional theory for nineteen structurally distinct candidate complexes. Further, the results showed that in all cases the binding energy was of the order of 1.5 eV, but depended on the specific bonding site. The most stable structure involved coordination of the uranyl cation and two structurally distinct oxygens in the Zr-MOF metal node. It was also found that higher degree of deprotonation in Zr-MOF correlated with higher binding energy between the Zr-MOF and uranyl cations. These insights can aid in the design of Zr-MOFs with optimized features for efficient capture of uranyl cations.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Analysis and prediction of reaction kinetics using the degree of rate control

“Degree of rate control” (DRC) analysis provides a quantitative approach for analysing the kinetics of multi-step reaction mechanisms that has been widely applied to both heterogeneous and homogeneous catalysis research, as well as electrocatalysis. The DRC of any given transition state or intermediate is defined as a partial derivative such that it approximately equals the fractional increase in net rate to the product of interest per differential decrease in its standard-state free energy for that species (÷RT), holding constant the standard-state free energies of all other transition states and intermediates. Even very complex mechanisms usually have only a few species with non-zero DRCs and are thus the species whose interactions with the catalyst most strongly affect the net rate. These key DRC values thus offer a simple and intuitive route to optimize catalyst materials, especially with the assistance of computational methods like density functional theory (DFT). These high-DRC species are also the species whose energetics must be most accurately measured or calculated to achieve an accurate kinetic model for any reaction mechanism. In simple cases with a single “rate-determining step”, the DRC for its transition state (TS) is + 1. Catalyst-bound intermediates, on the other hand, often have negative DRCs equal to a small integer times their fractional occupancy of catalyst sites. The apparent activation energy equals a weighted average of the standard-state enthalpies (relative to reactants) of all the species (intermediates, transition states and products) in the reaction mechanism, each weighted by its DRC (+RT). It has been shown that the apparent transfer coefficient in electrocatalysis, an inverted form of the Tafel slope, is a weighted average of the number of electrons transferred to generate each intermediate or product species in the mechanism, weighted again by the DRC. Quantitative analysis of kinetic isotope effects (KIEs, or the ratio of net rates for different reactant isotopes) in complex mechanisms has shown that the logarithm of the KIE equals the weighted average over all species in the mechanism of the difference between the two isotopes in their standard-state free energies (÷RT), again weighted by the DRC. The reaction orders with respect to fluid-phase concentrations of reactants, products and intermediates have also been proven to be directly related to DRCs. Thus, there are numerous experimental observables which equate to short linear combinations of DRCs, so that combinations of experimental measurements might provide access to DRC values. Since its invention for transition states in 1994 and its generalization to include intermediates in 2009, the DRC has thus far mainly been calculated for microkinetic models based either entirely on DFT or on DFT with the key energies (i.e., those for high-DRC species) being fine-tuned to match experiments. The relationships summarized in this work provide new opportunities for using experiments earlier in the development and optimization of microkinetic models that require input from computational catalysis.

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

Phase Diagrams of Alloys and Their Hydrides via On-Lattice Graph Neural Networks and Limited Training Data

Efficient prediction of sampling-intensive thermodynamic properties is needed to evaluate material performance and permit high-throughput materials modeling for a diverse array of technology applications. To alleviate the prohibitive computational expense of high-throughput configurational sampling with density functional theory (DFT), surrogate modeling strategies like cluster expansion are many orders of magnitude more efficient but can be difficult to construct in systems with high compositional complexity. We therefore employ minimal-complexity graph neural network models that accurately predict and can even extrapolate to out-of-train distribution formation energies of DFT-relaxed structures from an ideal (unrelaxed) crystallographic representation. This enables the large-scale sampling necessary for various thermodynamic property predictions that may otherwise be intractable and can be achieved with small training data sets. Two exemplars, optimizing the thermodynamic stability of low-density high-entropy alloys and modulating the plateau pressure of hydrogen in metal alloys, demonstrate the power of this approach, which can be extended to a variety of materials discovery and modeling problems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗