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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Can electric fields drive chemistry for an aqueous microdroplet?

Abstract Reaction rates of common organic reactions have been reported to increase by one to six orders of magnitude in aqueous microdroplets compared to bulk solution, but the reasons for the rate acceleration are poorly understood. Using a coarse-grained electron model that describes structural organization and electron densities for water droplets without the expense of ab initio methods, we investigate the electric field distributions at the air-water interface to understand the origin of surface reactivity. We find that electric field alignments along free O–H bonds at the surface are ~16 MV/cm larger on average than that found for O–H bonds in the interior of the water droplet. Furthermore, electric field distributions can be an order of magnitude larger than the average due to non-linear coupling of intramolecular solvent polarization with intermolecular solvent modes which may contribute to even greater surface reactivity for weakening or breaking chemical bonds at the droplet surface.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Uncovering electron scattering mechanisms in NiFeCoCrMn derived concentrated solid solution and high entropy alloys

Abstract Whilst it has long been known that disorder profoundly affects transport properties, recent measurements on a series of solid solution 3d -transition metal alloys reveal two orders of magnitude variations in the residual resistivity. Using ab initio methods, we demonstrate that, while the carrier density of all alloys is as high as in normal metals, the electron mean-free-path can vary from ~10 Å (strong scattering limit) to ~10 3 Å (weak scattering limit). Here, we delineate the underlying electron scattering mechanisms responsible for this disparate behavior. While site-diagonal, spin dependent, potential scattering is always dominant, for alloys containing only Fe, Co, and Ni the majority-spin channel experiences negligible disorder scattering, thereby providing a short circuit, while for Cr/Mn containing alloys both spin channels experience strong disorder scattering due to an electron filling effect. Somewhat surprisingly, other scattering mechanisms—including displacement, or size effect, scattering which has been shown to strongly correlate with such diverse properties as yield strength—are found to be relatively weak in most cases.

36 MATERIALS SCIENCE↗
DOE OSTI2026

The critical role of intrinsic defects and many-body interactions on the stability of MnBi2Te4

Intrinsic antisite defects pose a major challenge to understanding and predicting the exotic properties of the layered topological magnetic insulator MnBi2Te4 (MBT). In this work, we study the origin of the abundance of intrinsic defects in MBT, including many-body defect–defect interactions and many-body electronic correlations. Until now, ab initio methods have struggled to explain thermodynamic stability and properties influenced by defect behavior in MBT. We model native Mn–Bi antisite defects in MBT at finite temperatures using a cluster expansion that includes defect–defect interactions. To overcome the limitations of conventional density functional theory (DFT), we introduce a hybrid approach that incorporates high-accuracy quantum Monte Carlo (QMC) calculations, introducing missing correlations. This strategy allows for accurate estimation of defect energetics and finite-temperature properties. We compute the configurational free energy, defect concentration, and configurational heat capacity, revealing a second-order order–disorder phase transition near the experimental synthesis temperature. Our study provides the first theoretical insight into the thermodynamics of intrinsic defects in MBT. The negative free energy relative to pristine MBT at synthesis temperatures indicates that Mn–Bi antisite formation is thermodynamically spontaneous. We also present a broadly applicable general framework for correcting low-level theoretical theories using highly accurate many-body corrections from QMC.

Ghaffar, Abdul [ORNL] (ORCID:0000000241190168)↗
DOE OSTI2026

Enhancing the efficiency of time-dependent density functional theory calculations of dynamic response properties

X-ray Thomson scattering (XRTS) constitutes an essential technique for diagnosing material properties under extreme conditions, such as high pressures and intense laser heating. Time-dependent density functional theory (TDDFT) is one of the most accurate available ab initio methods for modeling XRTS spectra, as well as a host of other dynamic material properties. However, strong thermal excitations, along with the need to account for variations in temperature and density as well as the finite size of the detector significantly increase the computational cost of TDDFT simulations compared to ambient conditions. In this work, we present a broadly applicable method for optimizing and enhancing the efficiency of TDDFT calculations. Our approach is based on a one-to-one mapping between the dynamic structure factor and the imaginary time density–density correlation function, which naturally emerges in Feynman’s path integral formulation of quantum many-body theory. Specifically, we combine rigorous convergence tests in the imaginary time domain with a constraints-based attenuation of narrow-band fluctuations to improve the efficiency of TDDFT modeling without the introduction of any significant bias. As a result, we can report a speed-up by up to an order of magnitude, thus substantially reducing the burden of computational cost required for XRTS analysis.

Moldabekov, Zhandos A. [Helmholtz-Zentrum Dresden-↗
DOE OSTI2024

Electromagnetic properties of indium isotopes illuminate the doubly magic character of 100 Sn

Understanding the nuclear properties in the vicinity of 100 Sn, which has been suggested to be the heaviest doubly magic nucleus with proton number Z equal to neutron number N, has been a long-standing challenge for experimental and theoretical nuclear physics. In particular, contradictory experimental evidence exists regarding the role of nuclear collectivity in this region of the nuclear chart. Here, we provide further evidence for the doubly magic character of 100 Sn by measuring the ground-state electromagnetic moments and nuclear charge radii of indium (Z = 49) isotopes as N approaches 50 from above using precision laser spectroscopy. Our results span almost the complete range between the two major closed neutron shells at N = 50 and N = 82 and reveal parabolic trends as a function of the neutron number, with a clear reduction towards these two closed neutron shells. Furthermore, a detailed comparison between our experimental results and numerical results from two complementary nuclear many-body frameworks (density functional theory and ab initio methods) exposes deficiencies in nuclear models and establishes a benchmark for future theoretical developments.

100Sn↗
DOE OSTI2025

Quasicrystal stability and nucleation kinetics from density functional theory

The aperiodic order of quasicrystals bridges the amorphous and crystalline regime, so it has remained unclear whether quasicrystals are metastable or stable phases of matter. Density functional theory is often used to evaluate thermodynamic stability, but quasicrystals are long-range aperiodic and their energies cannot be calculated using conventional ab initio methods. Here, in this work, we perform first-principles calculations on quasicrystal nanoparticles of increasing size, from which we can directly extrapolate their bulk and surface energies. Using this technique, we determine with high confidence that the icosahedral quasicrystals ScZn 7.33 and YbCd 5.7 are ground-state phases, thus revealing that translational symmetry is not a necessary condition for the zero-temperature stability of inorganic solids. Although we found the ScZn 7.33 quasicrystal to be thermodynamically stable, we show on a mixed thermodynamic and kinetic phase diagram that its solidification from the melt is limited by nucleation, which illustrates why even stable materials may be kinetically challenging to grow. Our techniques broadly open the door to first-principles investigations into the structure–bonding–stability relationships of aperiodic materials.

density functional theory↗
DOE OSTI2024

Coherence mapping to identify the intermediates of multi-channel dissociative ionization

Identifying the short-lived intermediates and reaction mechanisms of multi-channel radical cation fragmentation processes remains a current and important challenge to understanding and predicting mass spectra. We find that coherent oscillations in the femtosecond time-dependent yields of several product ions following ultrafast strong-field ionization represent spectroscopic signatures that elucidate their mechanism of formation and identify the intermediate(s) they originate from. Experiments on endo-dicyclopentadiene show that vibrational frequencies from various intermediates are mapped onto their resulting products. Aided by ab initio methods, we identify the vibrational modes of both the cleaved and intact molecular ion intermediates. These results confirm stepwise and concerted fragmentation pathways of the dicyclopentadiene ion. This study highlights the power of tracking the femtosecond dynamics of all product ions simultaneously and sheds further light onto one of the fundamental reaction mechanisms in mass spectrometry, the retro-Diels Alder reaction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules

One of the grand challenges in modern theoretical chemistry is designing and implementing approximations that expedite ab initio methods without loss of accuracy. Machine learning (ML) methods are emerging as a powerful approach to constructing various forms of transferable atomistic potentials. They have been successfully applied in a variety of applications in chemistry, biology, catalysis, and solid-state physics. However, these models are heavily dependent on the quality and quantity of data used in their fitting. Fitting highly flexible ML potentials, such as neural networks, comes at a cost: a vast amount of reference data is required to properly train these models. We address this need by providing access to a large computational DFT database, which consists of more than 20 M off equilibrium conformations for 57,462 small organic molecules. We believe it will become a new standard benchmark for comparison of current and future methods in the ML potential community.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

The addition of methanol to Criegee intermediates

Bimolecular reactions involving stabilized Criegee intermediates (SCI) have been the target of many studies due to the role these molecules play in atmospheric chemistry. Recently, kinetic rates for the addition reaction of the simplest SCI (formaldehyde oxide) and its methylated analogue (acetone oxide) with methanol were reported both experimentally and theoretically. We re-examine the energy profile of these reactions by employing rigorous ab initio methods. Optimized CCSD(T)/ANO1 geometries are reported for the stationary points along the reaction path. Energies are obtained at the CCSD(T)/CBS level of theory. Contributions of full triple and quadruple excitations are computed to assess the convergence of this method. Rate constants are obtained using conventional canonical transition state theory under the rigid rotor harmonic oscillator approximation and with the inclusion of a one-dimensional hindered rotor treatment. These corrections for internal rotations have a significant impact on computed kinetic rate constants. With this approach, we compute rate constants for the addition of methanol to formaldehyde oxide (H 2 COO) and acetone oxide [(CH 3 ) 2 COO] at 298.15 K as (1.2 ± 0.8) × 10 -13 and (2.8 ± 1.3) × 10 -15 cm 3 s -1 , respectively. Additionally, we investigate the temperature dependence of the rate constant, concluding that the transition state barrier height and tunneling contributions shape the qualitative behaviour of these reactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

A computational survey of semiconductors for power electronics

Power electronics (PE) are used to control and convert electrical energy in a wide range of applications from consumer products to large-scale industrial equipment. While Si-based power devices account for the vast majority of the market, wide band gap semiconductors such as SiC, GaN, and Ga 2 O 3 are starting to gain ground. However, these emerging materials face challenges due to either non-negligible defect densities, high synthesis and processing costs, or poor thermal properties. Here, we report on a broad computational survey aimed to identify promising materials for future power electronic devices beyond SiC, GaN, and Ga 2 O 3 . We consider 863 oxides, sulfides, nitrides, carbides, silicides, and borides that exhibit finite calculated band gaps. We utilize ab initio methods in conjunction with models for intrinsic carrier mobility, and critical breakdown field to compute the widely used Baliga figure of merit. We also compute the lattice thermal conductivity and use it as an additional screening parameter. In addition to correctly identifying known PE materials, our survey has revealed a number of promising candidates exhibiting the desirable combination of high figure of merit and high lattice thermal conductivity, which we propose for further experimental investigations.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Raman spectrum and polarizability of liquid water from deep neural networks

We introduce a scheme based on machine learning and deep neural networks to model the environmental dependence of the electronic polarizability in insulating materials. Application to liquid water shows that training the network with a relatively small number of molecular configurations is sufficient to predict the polarizability of arbitrary liquid configurations in close agreement with ab initio density functional theory calculations. In combination with a neural network representation of the interatomic potential energy surface, the scheme allows us to calculate the Raman spectra along 2-nanosecond classical trajectories at different temperatures for H 2 O and D 2 O. The vast gains in efficiency provided by the machine learning approach enable longer trajectories and larger system sizes relative to ab initio methods, reducing the statistical error and improving the resolution of the low-frequency Raman spectra. Decomposing the spectra into intramolecular and intermolecular contributions elucidates the mechanisms behind the temperature dependence of the low-frequency and stretch modes.

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

Structural evolution of two-dimensional silicates using a “bond-switching” algorithm

Silicates are the most abundant materials in the earth's crust. In recent years, two-dimensional (2D) versions of them grown on metal supports (known as bilayer silicates) have allowed their study in detail down to the atomic scale. These structures are self-containing. Furthermore, they are not covalently bound to the metal support but interact with it through van der Waals forces. Like their three-dimensional counterparts, the 2D-silicates can form both crystalline and vitreous structures. Furthermore, the interconversion between vitreous to crystalline structures has been experimentally observed at the nanoscale. While theoretical work has been carried out to try to understand these transformations, a limitation for ab initio methods, and even molecular dynamics methods, is the computational cost of studying large systems and long timescales. In this work, we present a simple and computationally inexpensive approach, that can be used to represent the evolution of bilayer silicates using a bond-switching algorithm. This approach allows reaching equilibrium ring size distributions as a function of a parameter that can be related to the ratio between temperature and the energy required for the bond-switching event. The ring size distributions are compared to experimental data available in the literature.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

Diffusion Monte Carlo evaluation of disiloxane linearisation barrier

The disiloxane molecule is a prime example of silicate compounds containing the Si-O-Si bridge. The molecule is of significant interest within the field of quantum chemistry, owing to the difficulty in theoretically predicting its properties. Herein, the linearisation barrier of disiloxane is investigated using a fixed-node diffusion Monte Carlo (FNDMC) approach, which is one of the most reliable ab initio methods in accounting for the electronic correlation. Calculations utilizing the density functional theory (DFT) and the coupled cluster method with single and double substitutions, including noniterative triples (CCSD(T)) are carried out alongside FNDMC for comparison. It is concluded that FNDMC successfully predicts the disiloxane linearisation barrier and does not depend on the completeness of the basis-set as much as DFT or CCSD(T), thus establishing its suitability.

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

The efficacy of Lewis affinity scale metrics to represent solvent interactions with reagent salts in all-inorganic metal halide perovskite solutions

Solvents employed in the solution processing of metal halide perovskites are known to play a key role in defining the morphology and properties of the resulting thin film, and thus the performance of perovskite solar cell devices. Accurate metrics are needed that are capable of differentiating among candidates, finding solvents that adequately solubilize the various precursor species in solution and facilitate the nucleation and growth of these materials. Existing metrics such as the unsaturated Mayer bond order (UMBO) and the Gutmann donor number (DN) have been tested for lead iodide perovskite systems; but there has yet to be a comprehensive study on their transferability to lead-free perovskite solutions. Here, we use ab initio methods (density functional theory) and regression analysis tools to study the usefulness of DN and BF 3 affinity scales in this regard. We compared the relative effectiveness of these scales to describe interactions between solvents and BXn perovskite salts of lead (Pb 2+ ), tin (Sn 2+ and Sn 4+ ), germanium (Ge 2+ ), bismuth (Bi 3+ ), and antimony (Sb 3+ and Sb 5+ ). The DN proved to be a better representation than the BF 3 of such interactions, reflecting the closer similarity of these species to the “parent” SbCl 5 Lewis acid than to BF 3 . In addition, we have uncovered the usefulness of the lithium cation affinity metric (LCA) to describe the strength of interactions between solvents and A-site cations (e.g. Na + , K + , Rb + and Cs + ) in all-inorganic metal halide perovskite solutions. We find that the coordination strengths of solvents towards species in all-inorganic metal halide perovskite solutions are best described by two different metrics with distinct modes of action: DN differentiates among BX n salt complexes, and LCA among A-site cation species. This revelation can help guide the choice of solvent to optimize processing conditions. It also emphasizes the importance of selecting solvents whose DN and LCA optimize coordination to key Lewis acid species in all-inorganic perovskite solutions.

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

Catlas: an automated framework for catalyst discovery demonstrated for direct syngas conversion

Catalyst discovery is paramount to support access to energy and key chemical feedstocks in a post fossil fuel era. Exhaustive computational searches of large material design spaces using ab initio methods like density functional theory (DFT) are infeasible. We seek to explore large design spaces at relatively low computational cost by leveraging large, generalized, graph-based machine learning (ML) models, which are pretrained and therefore require no upfront data collection or training. We present Catlas, a framework that distributes and automates the generation of adsorbate-surface configurations and ML inference of DFT energies to achieve this goal. Catlas is open source, making ML assisted catalyst screenings easy and available to all. To demonstrate its efficacy, we use Catlas to explore catalyst candidates for the direct conversion of syngas to multi-carbon oxygenates. For this case study, we explore 947 stable/metastable binary, transition metal intermetallics as possible catalyst candidates. On this subset of materials, we are able to predict the adsorption energy of key descriptors, *CO and *OH, with good accuracy (0.16, 0.14 eV MAE, respectively). Using the projected selectivity towards C 2+ oxygenates from an existing microkinetic model, we identified 144 candidate materials. For 10 promising candidates, DFT calculations reveal a good correlation with our assessment using ML. Finally, among the top elemental combinations were Pt–Ti, Pd–V, Ni–Nb, and Ti–Zn, all of which appear unexplored experimentally.

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

Reaction dynamics of S( 3 P) with 1,3-butadiene and isoprene: crossed-beam scattering, low-temperature flow experiments, and high-level electronic structure calculations

Sulfur atoms serve as key players in diverse chemical processes, from astrochemistry at very low temperature to combustion at high temperature. Building upon our prior findings, showing cyclization to thiophenes following the reaction of ground-state sulfur atoms with dienes, we here extend this investigation to include many additional reaction products, guided by detailed theoretical predictions. The outcomes highlight the complex formation of products during intersystem crossing (ISC) to the singlet surfaces. Here, we employed crossed-beam velocity map imaging and high-level ab initio methods to explore the reaction of S( 3 P) with 1,3-butadiene and isoprene under single-collision conditions and in low-temperature flows. For the butadiene reaction, our experimental results show the formation of thiophene via H 2 loss, a 2H-thiophenyl radical through H loss, and thioketene through ethene loss at a slightly higher collision energy compared to previous observations. Complementary Chirped-Pulse Fourier-Transform mmWave spectroscopy (CP-FTmmW) measurements in a uniform flow confirmed the formation of thioketene in the reaction at 20 K. For the isoprene reaction, we observed analogous products along with the 2H-thiophenyl radical arising from methyl loss and C 3 H 4 S (loss of ethene or H 2 + acetylene). CP-FTmmW detected the formation of thioformaldehyde via loss of 1,3-butadiene, again in the 20 K flow. Coupled-cluster calculations on the pathways found by the automated kinetic workflow code KinBot support these findings and indicate ISC to the singlet surface, leading to the generation of various long-lived intermediates, including 5-membered heterocycles.

Lang, Jinxin↗
DOE OSTI2019

The effects of site asymmetry on near-degenerate state-to-state vibronic mixing in flexible bichromophores

Laser-induced fluorescence excitation and dispersed fluorescence spectra of a model flexible bichromophore, 1,1-diphenylethane (DPE), have been recorded under jet-cooled conditions in the gas phase in the region near the first pair of near-degenerate excited states (S 1 and S 2 ). The S 1 and S 2 origin transitions have been identified at 37 397 and 37 510 cm –1 , a splitting of 113 cm –1 . This splitting is four times smaller than the excitonic splitting calculated by ab initio methods at the EOM-CCSD/cc-pVDZ level of theory (410 cm –1 ), which necessarily relies on the Born-Oppenheimer approximation. Dispersed fluorescence spectra provide a state-to-state picture of the vibronic coupling. Furthermore, these results are compared with the results of a multimode vibronic coupling model capable of treating chromophores in asymmetric environments.

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

AP-Net: An atomic-pairwise neural network for smooth and transferable interaction potentials

Intermolecular interactions are critical to many chemical phenomena, but their accurate computation using ab initio methods is often limited by computational cost. The recent emergence of machine learning (ML) potentials may be a promising alternative. Useful ML models should not only estimate accurate interaction energies but also predict smooth and asymptotically correct potential energy surfaces. However, existing ML models are not guaranteed to obey these constraints. Indeed, systemic deficiencies are apparent in the predictions of our previous hydrogen-bond model as well as the popular ANI-1X model, which we attribute to the use of an atomic energy partition. As a solution, we propose an alternative atomic-pairwise framework specifically for intermolecular ML potentials, and we introduce AP-Net—a neural network model for interaction energies. The AP-Net model is developed using this physically motivated atomic-pairwise paradigm and also exploits the interpretability of symmetry adapted perturbation theory (SAPT). We show that in contrast to other models, AP-Net produces smooth, physically meaningful intermolecular potentials exhibiting correct asymptotic behavior. Initially trained on only a limited number of mostly hydrogen-bonded dimers, AP-Net makes accurate predictions across the chemically diverse S66x8 dataset, demonstrating significant transferability. On a test set including experimental hydrogen-bonded dimers, AP-Net predicts total interaction energies with a mean absolute error of 0.37 kcal mol−1, reducing errors by a factor of 2–5 across SAPT components from previous neural network potentials. The pairwise interaction energies of the model are physically interpretable, and an investigation of predicted electrostatic energies suggests that the model “learns” the physics of hydrogen-bonded interactions.

Glick, Zachary L. (ORCID:0000000309002849)↗