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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 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Nuclear–Electronic Orbital General Rate Theory: Predicting Hydrogen Kinetic Isotope Effects in the Deep Tunneling Regime

Hydrogen transfer is a critical component of many chemical and biological processes. The ratio of rate constants for hydrogen and deuterium transfer defines the H/D kinetic isotope effect (KIE), which is a powerful tool for elucidating hydrogen transfer mechanisms. Interpretation of experimental H/D KIEs relies on accurate and affordable computational methods. However, due to their light mass, hydrogen and deuterium can undergo tunneling, which is challenging to describe in multidimensional molecular systems. Herein, we introduce the nuclear–electronic orbital general rate theory (NEO-GRT), which enables the efficient prediction of H/D KIEs based on full-dimensional molecular quantum chemistry calculations. The NEO-GRT approach describes the hydrogen transfer rate constant with a general expression that spans the vibrationally adiabatic and nonadiabatic hydrogen tunneling regimes. The input quantities are computed using NEO density functional theory, which treats the transferring hydrogen or deuterium nucleus quantum mechanically on the same level as the electrons. We investigate two intramolecular proton transfer reactions in organic molecules at temperatures down to 50 K to evaluate the performance of NEO-GRT by comparison to transition state theory and ring-polymer instanton theory. The KIEs computed with NEO-GRT agree with those calculated using ring-polymer instanton theory for the full-dimensional molecular systems at the same level of electronic structure theory. This agreement indicates that NEO-GRT captures the deep hydrogen tunneling effects, in contrast to transition state theory, which neglects such effects. Given its relatively low computational cost, NEO-GRT is a promising approach for predicting H/D KIEs in large organic and organometallic systems.

Hydrogen↗
DOE OSTI2020

Microsecond Molecular Dynamics Simulations of Proteins Using a Quasi-Equilibrium Solvation Shell Model

In this work, we describe the development and implementation of a quasi-equilibrium hydration shell model of biomolecular solvation with adaptive boundaries. Applying the model to microsecond-long molecular dynamics simulations of several protein systems of varying complexity, we find that the model simulation results are of comparable quality to those obtained from simulations of fully solvated systems, but at a reduced computational cost. We discuss the dominant sources of error in the model and outline directions for future improvements.

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

CASSCF with Extremely Large Active Spaces Using the Adaptive Sampling Configuration Interaction Method

The complete active space self-consistent field (CASSCF) method is the principal approach employed for studying strongly correlated systems. However, exact CASSCF can only be performed on small active spaces of ~20 electrons in ~20 orbitals due to exponential growth in the computational cost. Here, we show that employing the Adaptive Sampling Configuration Interaction (ASCI) method as an approximate Full CI solver in the active space allows CASSCF-like calculations within chemical accuracy (<1 kcal/mol for relative energies) in active spaces with more than ~50 active electrons in ~50 active orbitals, significantly increasing the sizes of systems amenable to accurate multiconfigurational treatment. The main challenge with using any selected CI-based approximate CASSCF is the orbital optimization problem; they tend to exhibit large numbers of local minima in orbital space due to their lack of invariance to active–active rotations (in addition to the local minima that exist in exact CASSCF). We highlight methods that can avoid spurious local extrema as a practical solution to the orbital optimization problem. We employ ASCI-SCF to demonstrate a lack of polyradical character in moderately sized periacenes with up to 52 correlated electrons and compare against heat-bath CI on an iron porphyrin system with more than 40 correlated electrons.

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

Ground State Energy Functional with Hartree–Fock Efficiency and Chemical Accuracy

We introduce the deep post Hartree–Fock (DeePHF) method, a machine learning-based scheme for constructing accurate and transferable models for the ground-state energy of electronic structure problems. DeePHF predicts the energy difference between results of highly accurate models such as the coupled cluster method and low accuracy models such as the Hartree–Fock (HF) method, using the ground-state electronic orbitals as the input. It preserves all the symmetries of the original high accuracy model. The added computational cost is less than that of the reference HF or DFT and scales linearly with respect to system size. We examine the performance of DeePHF on organic molecular systems using publicly available data sets and obtain the state-of-art performance, particularly on large data sets.

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

Metal-to-Ligand Charge-Transfer Spectrum of a Ru-Bipyridine-Sensitized TiO 2 Cluster from Embedded Multiconfigurational Excited-State Theory

Understanding optical properties of the dye molecule in dye-sensitized solar cells (DSSCs) from first-principles quantum mechanics can contribute to improving the efficiency of such devices. While density functional theory (DFT) and time-dependent DFT have been pivotal in simulating optoelectronic properties of photoanodes used in DSSCs at the atomic scale, questions remain regarding DFT’s adequacy and accuracy to furnish critical information needed to understand the various excited-state processes involved. In this work, we simulate the absorption spectra of a dye-sensitized solar cell analogue, comprised of a Ru-bipyridine (Ru-bpy) dye molecule and a small TiO2 cluster via DFT and via an accurate embedded correlated wavefunction (CW) theory. We generated CW spectra for the adsorbed Ru-bpy dye via a recently introduced capped density functional embedding theory or capped-DFET (to generate the embedding potential that accounts for the interaction of the molecule and the TiO 2 cluster). We then combined capped-DFET with the accurate but expensive multiconfigurational complete active space second-order perturbation theory (CASPT2)—embedded CASPT2. Because the CW theory is conducted on only a portion of the total system in the presence of an embedding potential that describes that portion’s interaction with its environment, we efficiently obtain CW-quality predictions that reflect local properties of the entire system. Specifically, for example, with capped-DFET and embedded CW theory, we can simulate accurately a plethora of metal-to-ligand charge-transfer excited properties at a manageable computational cost. Here, we predict detailed electronic spectra within the visible region, featuring the lowest three singlet and triplet excited states, along with predictions of the singlets’ lifetimes. We illustrated these results using a Jablonski diagram that show the relative energy position of the singlet and longer-lived triplet excited states and analyzed and proposed relaxation paths for the excited state corresponding to the most intense but short-lived absorption (interconversion, intersystem crossing, fluorescence, and phosphorescence) that may lead to longer-lived excited states necessary for efficient charge separation required to generate current in solar cells.

14 SOLAR ENERGY↗
DOE OSTI2022

Simultaneous Optimization of Nuclear–Electronic Orbitals

Accurate modeling of important nuclear quantum effects, such as nuclear delocalization, zero-point energy, and tunneling, as well as non-Born-Oppenheimer effects, requires treatment of both nuclei and electrons quantum mechanically. The nuclear–electronic orbital (NEO) method provides an elegant framework to treat specified nuclei, typically protons, on the same level as the electrons. In conventional electronic structure theory, finding a converged ground state can be a computationally demanding task; converging NEO wavefunctions, due to their coupled electronic and nuclear nature, is even more demanding. Herein, we present an efficient simultaneous optimization method that uses the direct inversion in the iterative subspace method to simultaneously converge wavefunctions for both the electrons and quantum nuclei. In conclusion, benchmark studies show that the simultaneous optimization method can significantly reduce the computational cost compared to the conventional stepwise method for optimizing NEO wavefunctions for multicomponent systems.

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

Capturing the Coverage Dependence of Aromatics’ Adsorption through Mean-Field Models

To capture the dominant interactions (surface-mediated and through-space steric) in catalytic hydrodeoxygenation systems, coverage-dependent mean-field models of aromatic adsorption are developed on Pt(111) and Ru(0001). We derive three key insights from this work: (1) we can universally apply mean-field models to capture the coverage-dependent behavior of oxygenated aromatics on transition metal surfaces, (2) we can deconvolute surface-mediated and throughspace steric interactions from the mean-field model, and (3) we can develop relatively accurate models that predict the adsorption energy of aromatics on transition metal surfaces for the full coverage range using the work function at lowest modeled coverage. In conclusion, our approach enables the rapid prediction of coverage-dependent behavior of oxygenated aromatics on transition metal surfaces, reducing the computational cost associated with these studies by an order of magnitude.

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

Multiconfiguration Pair-Density Functional Theory for Vertical Excitation Energies in Actinide Molecules

Modeling actinides with electronic structure theories is challenging because these systems present a strong ligand field and metal–ligand covalency. Here, we systematically investigate the effectiveness of pair-density functional theory (PDFT) for the calculation of vertical excitation energies in An(III), [An III Cl 6 ] 3– , and [An VI O 2 ] 2+ (An = U, Np, Pu, and Am). We compare the performance of PDFT, hybrid PDFT, and multistate PDFT with traditional active-space methods followed by perturbation theory, like multistate CASPT2, and with experimental data. Overall, multistate PDFT gives quantitative agreement with multistate CASPT2 at a significantly reduced computational cost.

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

Spatial Signatures of Electron Correlation in Least-Squares Tensor Hypercontraction

Least Squares Tensor Hypercontraction (LS-THC) has received some attention in recent years as an approach to reduce the significant computational costs of wavefunc- tion based methods in quantum chemistry. However, previous work has demonstrated that the LS-THC factorization performs disproportionately worse in the description of wavefunction components (e.g. cluster amplitudes T 2 ) than Hamiltonian compo- nents (e.g. electron repulsion integrals (pq|rs)). This work develops novel theoretical methods to study the source of these errors in the context of the real-space T 2 kernel, and reports, for the first time, the existence of a “correlation feature” in the errors of the LS-THC representation of the “exchange-like” correlation energy EX and T 2 that is remarkably consistent across ten molecular species, three correlated wavefunctions, and four basis sets. This correlation feature portends the existence of a “pair-point kernel” missing in the usual LS-THC representation of the wavefunction, which critically depends upon pairs of grid points situated close to atoms and with inter-pair distances between one and two Bohr radii. These findings point the way for future LS-THC developments to address these shortcomings.

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

Computation of Auger Electron Spectra in Organic Molecules with Multiconfiguration Pair-Density Functional Theory

Efficient and accurate computation of molecular Auger electron spectra for larger systems is limited by the rapid increase in the number of doubly ionized final states as the system size grows. Here, in this work, we benchmark the application of multiconfiguration pair-density functional theory with a restricted active space (RAS) reference wave function for computing the carbon K-edge decay spectra of 20 organic molecules. Decay rates are computed within the one-center approximation. We evaluate the performance of different basis sets and on-top functionals and find that multiconfiguration pair-density functional theory achieves accuracy comparable to RAS followed by second-order perturbation theory, but at significantly lower computational cost.

Fouda, Adam E. A. [Argonne National Laboratory (AN↗
DOE OSTI2021

Using Neural Network Force Fields to Ascertain the Quality of Ab Initio Simulations of Liquid Water

Accurately simulating the properties of bulk water, despite the apparent simplicity of the molecule, is still a challenge. In order to fully understand and reproduce its complex phase diagram, it is necessary to perform simulations at the ab initio level, including quantum mechanical effects both for electrons and nuclei. This comes at a high computational cost, given that the structural and dynamical properties tend to require long timescales and large simulation cells. In this work, we evaluate the errors that density functional theory (DFT)-based simulations routinely incur into due time- and size-scale limitations. These errors are evaluated using neural-network-trained force fields that are accurate at the level of DFT methods. We compare different exchange and correlation potentials for properties of bulk water that require large timescales. Here we show that structural properties are less dependent on the system size and that dynamical properties such as the diffusion coefficient have a strong dependence on the simulation size and timescale. Our results facilitate comparisons of DFT-based simulation results with experiments and offer a path to discriminate between model and convergence errors in these simulations.

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

Neural Network Water Model Based on the MB-Pol Many-Body Potential

The MB-pol many-body potential accurately predicts many properties of water, including cluster, liquid phase, and vapor–liquid equilibrium properties, but its high computational cost can make applying it in large-scale simulations quite challenging. Here, in order to address this limitation, we developed a “deep potential” neural network (DPMD) model based on the MB-pol potential for water. We find that a DPMD model trained on mostly liquid configurations yields a good description of the bulk liquid phase but severely underpredicts vapor–liquid coexistence densities. By contrast, adding cluster configurations to the neural network training set leads to a good agreement for the vapor coexistence densities. Liquid phase densities under supercooled conditions are also represented well, even though they were not included in the training set. These results confirm that neural network models can combine accuracy and transferability if sufficient attention is given to the construction of a representative training set for the target system.

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

Ab Initio Modeling of Aqueous Methanol Mixtures at DFT-SCAN Level Using Machine Learning Interatomic Potentials

Abstract Methanol–water mixtures find use in many applications, particularly catalytic energy conversion processes. Their importance has motivated numerous computational studies, most of which employed molecular dynamics based on classical force fields. These enable simulations of large systems on long time scales but do not reliably describe reactive dynamics involving bond breaking and bond formation. In contrast, ab initio molecular dynamics (AIMD) based on density functional theory (DFT) is generally more reliable for such applications but has a high computational cost, which discourages systematic studies of alcohol-water mixtures. To remedy this, we trained a machine learning interatomic potential capable of probing the properties of aqueous methanol mixtures at the DFT level using the SCAN functional. Our results show that SCAN qualitatively reproduces multiple key experimental features arising from the amphiphilic nature of methanol, including density, diffusion coefficients, X-ray structure factors, and Kirkwood–Buff integrals. We also find that structural correlations between water molecules are somewhat overestimated, leading to a stronger preferential association than that predicted by experiments. However, increasing the temperature by 30 K mitigates this effect and also recovers the correct mobilities of both methanol and water. These results indicate that SCAN provides an accurate description of methanol–water mixtures, making it a reliable choice for investigating the reactive dynamics in such systems.

Park, Sanghyun J. [Princeton University , , , ,]↗
DOE OSTI2020

Enabling Efficient and Accurate Computational Studies of MOF Reactivity via QM/MM and QM/QM Methods

Electronic structure calculations can provide unique insight into metal–organic framework (MOF) reactivity and defect formation. Such calculations can be broadly categorized as utilizing either periodic or cluster models, each with their respective advantages and disadvantages. In the present study, we demonstrate how multiscale methods can leverage the advantages of both approaches to enable high levels of accuracy and computational efficiency in studies of MOF reactivity. Using defect formation in a zeolitic imidazolate framework (ZIF) as a prototypical example, we benchmark a quantum mechanics/molecular mechanics (QM/MM) scheme that enables highly efficient cluster-based calculations on MOFs. We demonstrate the importance of correctly accounting for the influence of both dative bond cleavage in the QM cluster and long-range mechanical coupling to the bulk to achieving accurate QM/MM studies of MOFs. We subsequently leverage these cluster models in a QM/QM scheme that goes beyond standard DFT to yield “gold-standard” correlated wave function results on MOFs at modest computational cost. Crucially, we find several cases in which the incorporation of these correlated corrections yields qualitatively important corrections over conventional DFT values.

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

Computation of the Thermal Expansion Coefficient of Graphene with Gaussian Approximation Potentials

Direct experimental measurement of thermal expansion coefficient without substrate effects is a challenging task for two-dimensional (2D) materials, and its accurate estimation with large-scale ab initio molecular dynamics is computationally very expensive. Machine learning-based interatomic potentials trained with ab initio data have been successfully used in molecular dynamics simulations to decrease the computational cost without compromising the accuracy. In this work, we investigated using Gaussian approximation potentials to reproduce the density functional theory-level accuracy for graphene within both lattice dynamical and molecular dynamical methods, and to extend their applicability to larger length and time scales. Two such potentials are considered, GAP17 and GAP20. GAP17, which was trained with pristine graphene structures, is found to give closer results to density functional theory calculations at different scales. Further vibrational and structural analyses verify that the same conclusions can be deduced with density functional theory level in terms of the reasoning of the thermal expansion behavior, and the negative thermal expansion behavior is associated with long-range out-of-plane phonon vibrations. Thus, it is argued that the enabled larger system sizes by machine learning potentials may even enhance the accuracy compared to small-size-limited ab initio molecular dynamics.

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

Adsorbate Free Energies from DFT-Derived Translational Energy Landscapes

Adsorption free energies are fundamental to surface chemistry and catalysis. Standard models combine some assumed analytical form of the translational potential energy surface, often parameterized against density functional theory (DFT) calculations, with an analytical expression for the resultant translational densities of states (DOS), free energy, and entropy. Here we compare the performance of such models against numerical evaluations of the DOS and thermodynamic functions derived from solutions to the translational Schrodinger equation. We compare results for a translational potential energy surface (PES) derived from nudged eleastic band calculations with those obtained from adsorbate rastering across a series of monatomic (O, S, C, N, H) and polyatomic (NH x ) adsorbates on (100) Pt and Au facets. We find that analytical models as commonly parameterized have mixed performance for describing the translational PES and that the consequences for computed free energies are modest but potentially significant in microkinetic models. Here, numerical solutions are possible for modest to no additional computational cost over analytical models and thus should be considered when reliable free energy estimates are needed or translational potential energy surfaces are available.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Monte Carlo Explicitly Correlated Second-Order Many-Body Green’s Function Calculations of Semiconductor Band Gaps

A systematically converging series of ab initio, post-density-functional, size-consistent, electron-correlated approximations is desired for predictive computing of felectronic band structures of insulating, semiconducting, and metallic solids. A series that meets all of these desiderata (except the applicability to metals) is ab initio many-body Green's function theory based on Gaussian-type-orbital (GTO) basis sets. Here, its leading-order approximation, the second-order Green's function (GF2) method in the diagonal and frequency-independent approximations with the aug-cc-pVDZ basis set, is applied to the fundamental band gaps of three semiconductors (diamond, silicon, and silicon carbide in the zincblende structure) using cluster models. Corrections are made to the basis-set-incompleteness errors by the explicit-correlation (F12) ansatz (GF2-F12) for the valence band edges. The crystals are modeled as surface-passivated clusters of increasing sizes, whose wave functions are expanded by up to 2709 GTO basis functions. Immense computational costs of these calculations are overcome by the highly scalable stochastic algorithm of the Monte Carlo GF2-F12 method, whose operation cost per state increases only as a cubic power of system size, which has a tiny memory footprint and easily achieves near-perfect parallel efficiency on thousands of CPUs or on hundreds of GPUs. The correlated, F12-corrected highest-occupied and lowest-unoccupied molecular-orbital energy (HOMO-LUMO) gap is 5.78 ± 0.07 eV for C 87 H 76 as compared with the experimental value of the fundamental (indirect) band gap of bulk diamond at 5.48 eV. The correlated, F12-corrected HOMO-LUMO gaps for Si 75 H 76 and Si 32 C 43 H 76 are 2.56 ± 0.15 eV and 3.50 ± 0.12 eV, respectively, which are expected to decrease further with increasing cluster sizes. As a result, the experimental fundamental (indirect) band gaps of bulk silicon and silicon carbide are 1.17 eV and 2.42 eV, respectively.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Application of First-Principles-Based Artificial Neural Network Potentials to Multiscale-Shock Dynamics Simulations on Solid Materials

The use of artificial neural network (ANN) potentials trained with first-principles calculations has emerged as a promising approach for molecular dynamics (MD) simulations encompassing large space and time scales while retaining first-principles accuracy. To date, however, the application of ANN-MD has been limited to near-equilibrium processes. Here we combine first-principles-trained ANN-MD with multiscale shock theory (MSST) to successfully describe far-from-equilibrium shock phenomena. Overall, our ANN-MSST-MD approach describes shock-wave propagation in solids with first-principles accuracy but a 5000 times shorter computing time. Accordingly, ANN-MD-MSST was able to resolve fine, long-time elastic deformation at low shock speed, which was impossible with first-principles MD because of the high computational cost. This work thus lays a foundation of ANN-MD simulation to study a wide range of far-from-equilibrium processes.

36 MATERIALS SCIENCE↗