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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Computational catalyst discovery: Active classification through myopic multiscale sampling

We report the recent boom in computational chemistry has enabled several projects aimed at discovering useful materials or catalysts. We acknowledge and address two recurring issues in the field of computational catalyst discovery. First, calculating macro-scale catalyst properties is not straightforward when using ensembles of atomic-scale calculations [e.g., density functional theory (DFT)]. We attempt to address this issue by creating a multi-scale model that estimates bulk catalyst activity using adsorption energy predictions from both DFT and machine learning models. The second issue is that many catalyst discovery efforts seek to optimize catalyst properties, but optimization is an inherently exploitative objective that is in tension with the explorative nature of early-stage discovery projects. In other words, why invest so much time finding a “best” catalyst when it is likely to fail for some other, unforeseen problem? We address this issue by relaxing the catalyst discovery goal into a classification problem: “What is the set of catalysts that is worth testing experimentally?” Here, we present a catalyst discovery method called myopic multiscale sampling, which combines multiscale modeling with automated selection of DFT calculations. It is an active classification strategy that seeks to classify catalysts as “worth investigating” or “not worth investigating” experimentally. Our results show an ~7–16 times speedup in catalyst classification relative to random sampling. These results were based on offline simulations of our algorithm on two different datasets: a larger, synthesized dataset and a smaller, real dataset.

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

Exact-two-component block-localized wave function: A simple scheme for the automatic computation of relativistic ΔSCF

Block-localized wave function is a useful method for optimizing constrained determinants. In this article, we extend the generalized block-localized wave function technique to relativistic two-component framework. Optimization of excited state determinants for two-component wave functions presents a unique challenge because the excited state manifold is often quite dense with degenerate states. Furthermore, we test the degree to which certain symmetries result naturally from the ΔSCF optimization such as time reversal symmetry and symmetry with respect to the total angular momentum operator on a series of atomic systems. Variational optimizations may often break symmetry in order to lower the overall energy, just as unrestricted Hartree-Fock breaks spin symmetry. Overall, we demonstrate that time reversal symmetry is roughly maintained when using Hartree-Fock, but less so when using Kohn-Sham density functional theory. Additionally, maintaining total angular momentum symmetry appears to be system dependent and not guaranteed. Finally, we were able to trace the breaking of total angular momentum symmetry to the relaxation of core electrons.

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

Information Content of the Parity-Violating Asymmetry in Pb 208

The parity-violating asymmetry A PV in 208 Pb, recently measured by the PREX-2 Collaboration, is studied using modern relativistic (covariant) and nonrelativistic energy density functionals. We first assess the theoretical uncertainty on A PV which is intrinsic to the adopted approach. To this end, we use quantified functionals that are able to accommodate our previous knowledge on nuclear observables such as binding energies, charge radii, and the dipole polarizability α D of 208 Pb. We then add the quantified value of A PV together with α D to our calibration dataset to optimize new functionals. Based on these results, we predict a neutron skin thickness in 208 Pb r skin = 0.19 ± 0.02 fm and the symmetry-energy slope L = 54 ± 8 MeV. These values are consistent with other estimates based on astrophysical data and are significantly lower than those recently reported using a particular set of relativistic energy density functionals. Here, we also make a prediction for the A PV value in 48 Ca that will be soon available from the CREX measurement.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Mechanistic Insights and Rational Design of Ca-Doped CeO 2 Catalyst for Acetic Acid Ketonization

Carboxylic acid ketonization has recently gained significant attention to produce biomass-derived hydrocarbon fuels as it not only removes the highly reactive carboxylic functional group but also increases the size of the carbon chain. In this work, Ca-doped CeO 2 -based catalysts were investigated for acetic acid ketonization using a combined experimental and computational approach. Acetic acid conversion was performed across a range of temperatures including higher temperatures relevant to catalytic hot gas filtration (450 °C). Ca addition slightly decreases overall acetic acid ketonization reactivity yet stabilizes the catalyst at the higher temperatures necessary for catalytic hot gas filtration. From density functional theory calculations of the ketonization reaction mechanism, the C–C coupling and water formation steps are identified as two of the most energy-consuming steps on a CeO 2 surface with a proximal oxygen vacancy and the presence of a Ca dopant stabilizes the key intermediates. Calculations predict an optimal structure comprising three Ca ensembles to minimize the reaction free energies for C–C coupling and water formation steps. These findings provide a priori information to guide future experiments for ketonization catalyst design and development.

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

Redox Potentials for Hydrogen Species in Acetonitrile and Tetrahydrofuran

The properties of the fundamental hydrogen species, proton (H+), hydrogen atom (H•), and hydride anion (H-) are critical to a vast range of chemical processes, yet their thermodynamic properties in nonaqueous solvents are not well established. A hybrid supermolecule-continuum approach is used to predict the Gibbs free energies of solvation (?G°solv) and standard redox potentials (E°) for the 2H+/H2 and H•/H- couples in acetonitrile (MeCN) and tetrahydrofuran (THF) following the approach previously used for water. Gas phase geometry optimizations were done using density functional theory (DFT) with the ?B97XD functional, Møller-Plesset second-order perturbation theory (MP2) with augmented correlation-consistent basis sets and composite-correlated molecular orbital theory (G3MP2 and G3MP2B3). Additional single point calculations in gas phase were performed for H- affinity for MeCN and THF at the coupled cluster CCSD(T) level using MP2 geometries. Solvation was included using the self-consistent reaction field SMD model. For H+, the ?G°solv values are predicted to be -252.2 kcal/mol in MeCN and -261.0 kcal/mol in THF. The redox potentials for the 2H+/H2 couple are +0.49 V in MeCN and +0.11 V in THF relative to the aqueous SHE, consistent with the available experimental data. The ?G°solv for H- is predicted to be -80 kcal/mol in MeCN and -68 in THF, and for H•, ?G°solv is predicted to be -1.3 kcal/mol in THF and -2.0 kcal/mol in MeCN. These solvation energies yield respective calculated redox potentials for the H•/H- couple of -0.14 V in MeCN and -0.68 V in THF relative to the aqueous SHE. H2 solvation is slightly exergonic in MeCN (-3.3 kcal/mol) and near thermoneutral in THF (+1.3 kcal/mol). The pKa(H2) is predicted to be 45.4 in MeCN and 47.8 in THF. The proton-coupled electron transfer (PCET) accounting term CG was evaluated giving values on the ferrocene scale within approximately 2 kcal/mol of literature in both solvents. These results provide benchmarks for hydrogen redox chemistry in MeCN and THF and improve consistency across E°, ?G°solv, pKa, and CG. These results provide benchmarks for hydrogen redox chemistry in nonaqueous media challenging previous estimates based on indirect extrapolations and assumptions.

Duda, Damian P.↗
DOE OSTI2024

Tailoring the Electronic Structures and Spectral Properties of ZnO with Irradiation Defects Generated Under Intense Electronic Excitation: A Combined Experimental and DFT Approach

Here, the relationship between the composition of the internal defect states, spectral properties, and correlated electronic structures of wurtzite zinc oxide (ZnO) crystals under 645 MeV Xe 35+ irradiation is systematically investigated, employing experimental characterizations combined with first-principle calculations. Based on the ion irradiation-induced thermal expansion and relaxation processes, the high concentration of vacancy/interstitial defects produced from the transient disordered phase in molten track states trigger photoelectric changes, as follows: i) the generation of internal defect states effectively reduces the intrinsic bandgap (3.25 eV → 2.66 eV); ii) a large number of defective active sites inhibits the recombination between electron–hole pairs, causing dark conductance and photoconductance to increase with increasing damage levels until optimal fluence is achieved. Based on the density functional theory (DFT) with the GGA + U (GGA = generalized gradient approximation) method, the defective models associated with the different electronic structures, density of states, formation energy, and the nature of the chemical bonding are established. The narrowing of the bandgap observed experimentally and the enhancement of carrier concentration originating from the internal electron defect states are qualitatively verified, therefore laying the foundation for designing future nanoscale photoelectronic devices and microelectronics applications.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Architecting the Third Dimension of Electrochemical Energy Storage

Three-dimensional (3D) architectural design has emerged as a powerful strategy to push electrochemical energy storage (EES) devices beyond the intrinsic limitations of conventional two-dimensional (2D) electrodes. While planar architectures enable high packing density and mature manufacturing, they suffer from limited ion transport and low active-material loading. In contrast, 3D architectures introduce low-tortuosity networks and high surface area that enhance charge and mass transport while supporting thick, high mass-loading electrodes. However, their practicality remains hindered by challenges in volumetric density, mechanical stability, and large-scale manufacturability. Here, this Perspective examines the key evaluation and design principles that govern 3D device performance. We discuss the fundamental trade-offs between porosity, volumetric density, and mechanical stability that shape 3D design and highlight emerging strategies for integrating materials engineering, structural optimization, device integration, computational modeling, and scalable manufacturing. By aligning structural functionality with manufacturability, 3D architectures can evolve from laboratory prototypes to commercially viable energy storage systems.

25 ENERGY STORAGE↗
NASA NTRS1998

C2 Fragmentation Energy of C60 Revisited: Theory Disagrees with Most Experiments

Following our earlier work on the subject, we have carried out density functional theory (DFT) and second-order Moller-Plesset perturbation theory (MP2) calculations of the dissociation energy of the reaction C60 yields C58 + C2 using polarized basis sets and geometries optimized with DFT methods. The present theoretical results support an electronic fragmentation energy D(sub e) around 10-11 eV in disagreement with most experimental results that place the dissociation energy D(sub o) (including zero point energy) around 7-8 eV. The plausible errors remaining in the theoretical calculations are unlikely to account for this big difference (2-4 eV).

Boese, A. Daniel↗
NASA NTRS2021

Utilizing local phase transformation strengthening for nickel-based superalloys

Almost 75 years of research has been devoted to producing superalloys capable of higher operating temperatures in jet turbine engines, and there is an ongoing need to increase operating temperature further. Here, a new disk Nickel-base superalloy is designed to take advantage of strengthening atomic-scale dynamic complexions. This local phase transformation strengthening provides the alloy with a three times improvement in creep strength over similar disk superalloys and comparable strength to a single crystal blade alloy at 760 °C. Ultra-high-resolution chemical mapping reveals that the improvement in creep strength is a result of atomic-scale η (D024) and χ (D019) formation along superlattice stacking faults. To understand these results, the energy differences between the L12 and competing D024 and D019 stacking fault structures and their dependence on composition are computed by density functional theory. This study can help guide researchers to further optimize local phase transformation strengthening mechanisms for alloy development.

Timothy M. Smith↗
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 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↗