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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 OSTI2026

A Chemist’s Guide to Solid-State Qubits: Diamond Color Centers as Embedded Vacancy-Centered Molecules

Diamond point defects or color centers are at the crux of solid-state quantum technology applications, from computing to sensing. Modeling molecular qubits has advanced by leaps and bounds mainly due to the advanced quantum mechanical (QM) tools in the arsenal of quantum chemists for modeling (isolated) molecular systems, which should be easily translatable for analogous systems in the solid state. Here, we simulate a series of diamond defects: a carbon vacancy (V C ) and second row substitutions that are adjacent to a V C or D C V C , with D = B, N, and O, to evaluate their magnetic and optical properties. We applied molecular orbital principles, an embedded cluster method, and a multiconfigurational QM theory to computationally characterize the above-mentioned defects for a multitude of spin, charge, and excited states. Our computational approach delivered high-quality QM insights that compare well with experiments. Furthermore, with this framework, we confirm the spin-triplets B C V C – and O C V C 0 to be analogous to N C V C – , with calculated bright transitions that respectively correspond to UV (3.80 eV) and blue (2.65 eV) light absorption and emission lifetimes (τ) of 2.3 and 5.6 ns, which are higher in energy and more short-lived (and thus brighter) than that for N C V C – : green (2.26 eV), τ = 9.5 ns.

Martirez, John Mark P. [Princeton Plasma Physics L↗
DOE OSTI2024

Photoinduced hydrogen dissociation in thymine predicted by coupled cluster theory

The fate of thymine upon excitation by ultraviolet radiation has been the subject of intense debate. Today, it is widely believed that its ultrafast excited state gas phase decay stems from a radiationless transition from the bright ππ* state to a dark nπ* state. However, conflicting theoretical predictions have made the experimental data difficult to interpret. Here we simulate the early gas phase ultrafast dynamics in thymine at the highest level of theory to date. This is made possible by performing wavepacket dynamics with a recently developed coupled cluster method. Our simulation confirms an ultrafast ππ* to nπ* transition (τ = 41 ± 14 fs). Furthermore, the predicted oxygen-edge X-ray absorption spectra agree quantitatively with experiment. We also predict an as-yet uncharacterized πσ* channel that leads to hydrogen dissociation at one of the two N-H bonds. Similar behavior has been identified in other heteroaromatic compounds, including adenine, and several authors have speculated that a similar pathway may exist in thymine. However, this was never confirmed theoretically or experimentally. This prediction calls for renewed efforts to experimentally identify or exclude the presence of this channel.

Kjønstad, Eirik F.↗
DOE OSTI2020

In search of molecular ions for optical cycling: a difficult road

Optical cycling, a continuous photon scattering off atoms or molecules, plays a central role in the quantum information science. While optical cycling has been experimentally achieved for many neutral species, few molecular ions have been investigated. We present a systematic theoretical search for diatomic molecular ions suitable for optical cycling using equation-of-motion coupled-cluster methods. Inspired by the electronic structure patterns of laser-cooled neutral molecules, we establish the design principles for molecular ions and explore various possible cationic molecular frameworks. The results show that finding a perfect molecular ion for optical cycling is challenging, yet possible. Among various possible diatomic molecules we suggest several candidates, which require further attention from both theory and experiment: YF + , SiO + , PN + , SiBr + , and BO + .

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

Theoretical study of the CO 2 –O 2 van der Waals complex: potential energy surface and applications

A four-dimensional-potential energy surface (4D-PES) of the atmospherically relevant carbon dioxide–oxygen molecule (CO 2 –O 2 ) van der Waals complex is mapped using the ab initio explicitly correlated coupled cluster method with single, double, and perturbative triple excitations (UCCSD(T)-F12b), and extrapolation to the complete basis set (CBS) limit using the cc-pVTZ-F12/cc-pVQZ-F12 bases and the l –3 formula. An analytic representation of the 4D-PES was fitted using the method of interpolating moving least squares (IMLS). These calculations predict that the most stable configuration of CO 2 –O 2 complex corresponds to a planar slipped-parallel structure with a binding energy of V ~ –243 cm –1 . Another isomer is found on the PES, corresponding to a non-planar cross-shaped structure, with V ~ –218 cm –1 . The transition structure connecting the two minima is found at V ~ –211 cm –1 . We also performed comparisons with some CO 2 –X van der Waals complexes. Moreover, we provide a SAPT analysis of this molecular system. Then, we discuss the complexation induced shifts of CO 2 and O 2 . Afterwards, this new 4D-PES is employed to compute the second virial coefficient including temperature dependence. A comparison between quantities obtained in our calculations and those from experiments found close agreement attesting to the high quality of the PES and to the importance of considering a full description of the anisotropic potential for the derivation of thermophysical properties of CO 2 –O 2 mixtures.

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

Atomic-scale electronic inhomogeneity in single-layer iron chalcogenide alloys revealed by machine learning of STM/S data

Chemical pressure from the isovalent substitution of Se by a larger Te atom in the epitaxial film of iron chalcogenide FeSe can effectively tune its superconducting, topological, and magnetic properties. However, such substitution during epitaxial growth inherently leads to defects and structural inhomogeneity, making the determination of alloy composition and atomic sites for the substitutional Te atoms challenging. Here, we utilize machine learning to distinguish between Se and Te atoms in scanning tunneling microscopy images of single-layer FeSe1−xTex on SrTiO3(001) substrates. Defect locations are first identified by analyzing spatial-dependent dI/dV tunneling spectra using the K-means clustering method. After excluding the defect regions, the remaining dI/dV spectra are further analyzed using the singular value decomposition method to determine the Se/Te ratio. Our findings demonstrate an effective and reliable approach for determining alloy composition and atomic-scale electronic inhomogeneity in superconducting single-layer iron chalcogenide films.

Materials Science↗
DOE OSTI2026

Enhancing transfer learning in angle-resolved photoemission spectroscopy (ARPES) with spatially-aware representations via graph convolution

A recent application of machine learning has been to spatially-resolved angle-resolved photoemission spectroscopy (ARPES). Here we advance the state-of-the-art by applying representational learning to transform ARPES data into an embedding space of a pre-trained self-supervised learning model, thus enhancing the pipeline that improves the bandstructure classification and domain assignment/segmentation performance compared to a k-means clustering method. In the current iteration, the real-space information is entered into the domain assignment through the graph convolution method, which improves the transfer learning performance of the original self-supervised model. Lastly, an unsupervised automated tool is developed that incorporates these techniques to enable automatic domain assignment.

ARPES↗
DOE OSTI2023

Conserved unique peptide patterns (CUPP) online platform 2.0: implementation of +1000 JGI fungal genomes

Carbohydrate-processing enzymes, CAZymes, are classified into families based on sequence and three-dimensional fold. Because many CAZyme families contain members of diverse molecular function (different EC-numbers), sophisticated tools are required to further delineate these enzymes. Such delineation is provided by the peptide-based clustering method CUPP, Conserved Unique Peptide Patterns. CUPP operates synergistically with the CAZy family/subfamily categorizations to allow systematic exploration of CAZymes by defining small protein groups with shared sequence motifs. The updated CUPP library contains 21,930 of such motif groups including 3,842,628 proteins. The new implementation of the CUPP-webserver, https://cupp.info/, now includes all published fungal and algal genomes from the Joint Genome Institute (JGI), genome resources MycoCosm and PhycoCosm, dynamically subdivided into motif groups of CAZymes. This allows users to browse the JGI portals for specific predicted functions or specific protein families from genome sequences. Thus, a genome can be searched for proteins having specific characteristics. All JGI proteins have a hyperlink to a summary page which links to the predicted gene splicing including which regions have RNA support. The new CUPP implementation also includes an update of the annotation algorithm that uses only a fourth of the RAM while enabling multi-threading, providing an annotation speed below 1 ms/protein.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Charge radii of exotic neon and magnesium isotopes

We compute the charge radii and ground-state energies of even-mass neon and magnesium isotopes from neutron number N = 8 to the dripline. Our calculations are based on nucleon-nucleon and three-nucleon potentials from chiral effective field theory that include Δ isobars. These potentials yield an accurate saturation point and symmetry energy of nuclear matter. We use the coupled-cluster method and start from an axially symmetric reference state. Binding energies and two-neutron separation energies largely agree with data, and the dripline in neon is accurate. The computed charge radii are accurate for many isotopes where data exist. Finer details, such as isotope shifts, however, are not accurately reproduced. These chiral potentials indicate a subshell closure at N = 14 for the radii (but not for two-neutron separation energies) and a decrease in charge radii at N = 8 (observed in neon and predicted for magnesium). Furthermore, they yield a continued increase of charge radii as neutrons are added beyond N = 14 yet underestimate the large increase at N = 20 in magnesium.

20 ≤ A ≤ 38↗
DOE OSTI2023

Entanglement entropy of nuclear systems

We study entanglement entropies between the single-particle states of the hole space and its complement in nuclear systems. Analytical results based on the coupled-cluster method show that entanglement entropies are proportional to the particle number fluctuation and the depletion number of the hole space for sufficiently weak interactions. General arguments also suggest that the entanglement entropy in nuclear systems fulfills a volume instead of an area law. Here, we test and confirm these results by computing entanglement entropies of the pairing model and neutron matter, and the depletion number of finite nuclei.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Emulating ab initio computations of infinite nucleonic matter

We construct efficient emulators for the computation of the infinite nuclear matter equation of state. These emulators are based on the subspace-projected coupled-cluster method for which we here develop a new algorithm called small-batch voting to eliminate spurious states that might appear when emulating quantum many-body methods based on a non-Hermitian Hamiltonian. The efficiency and accuracy of these emulators facilitate a rigorous statistical analysis within which we explore nuclear matter predictions for > 10 6 different parametrizations of a chiral interaction model with explicit Δ -isobars at next-to-next-to leading order. Constrained by nucleon-nucleon scattering phase shifts and bound-state observables of light nuclei up to He 4 , we use history matching to identify nonimplausible domains for the low-energy coupling constants of the chiral interaction. Within these domains we perform a Bayesian analysis using sampling and importance resampling with different likelihood calibrations and study correlations between interaction parameters, calibration observables in light nuclei, and nuclear matter saturation properties. Published by the American Physical Society 2024

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Dynamics of Density Fluctuations in Atomic Nuclei

We study the spatiotemporal patterns of density fluctuations in 16,24O and 48Ca using nuclear interactions from chiral effective field theory and the time-dependent coupled-cluster method. We find that two-particle–two-hole excitations generate small-amplitude fluctuations that are fast, short-ranged, and of stochastic character.

Bonaiti, Francesca [ORNL] (ORCID:0000000239261609)↗
DOE OSTI2024

Joint Management and Optimization of Residential Natural Gas and Electricity Distribution Networks Coupled via Fuel Cells

The attractive features of natural gas as well as the growing electric power demand worldwide have created increasing interest in natural-gas-based distributed generation applications for electric distribution networks. Here, this paper investigates the interdependency between a residential natural gas network and an electric distribution network that are linked together via fuel cells. The modeling of the natural gas network is introduced first, and then the algorithm for gas flow study is presented. The optimal placement and sizing of fuel cell based distributed generation systems are formulated to minimize the losses in both the natural gas network and the electric distribution grid, subject to the constraints imposed by both networks. In addition, a probabilistic model for both gas and electricity demands is developed based on historical electricity and natural gas demand data. A K-means clustering method is used to determine the hourly load states to solve the joint probabilistic optimization problem. Simulation studies are carried out on an integrated system consisting of the IEEE 69-bus distribution network and a radial 27-node natural gas network to verify the developed optimization model and the proposed method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

A Zonal Volt/VAR Control Mechanism for High PV Penetration Distribution Systems

This paper presents a zonal Volt/VAR control scheme for regulating voltage in unbalanced 3-phase distribution systems using inverter-based resources (IBR). First, the dependency between nodal voltage changes and IBR reactive power injections is derived via voltage sensitivity studies. Then, a fast-incremental clustering method is used to divide the distribution circuit into weakly-coupled zones based on correlations between nodal voltage sensitivities. The weak-coupling allows the voltage to be regulated independently within each zone using a rule-based voltage controller to dispatch IBR for voltage corrections. Simulation results on actual distribution feeder show that the proposed zone-based Volt/VAR control method maintains system voltages within their operational limits while reducing the runtime of the Volt/VAR controller from tens of second to a couple of milliseconds compared with centralized, optimization-based Volt/VAR control methods.

Alrushoud, Asmaa↗
DOE OSTI2020

Robust System Separation Strategy Considering Online Wide-Area Coherency Identification and Uncertainties of Renewable Energy Sources

With the fast growth of renewable energy sources (RES), more and more uncertainties are involved and influencing the stable operation of power systems. Controlled islanding is the last measure to prevent power system blackouts, thus this paper aims to propose a novel model of system separation based on Online Coherency Identification and Adjustable Robust Optimization Programming (OCI-AROP) for minimizing load shedding considering the uncertainties of RES. First, Fuzzy C-Means (FCM) clustering method with F-statistics is utilized to identify the coherent generator groups with the frequency data measured by Phasor Measurement Units (PMUs). Then, the OCI-AROP model considering coherent group constraints, connectivity constraints and robustness constraints about RES are presented. Finally, the case studies on IEEE-39 bus system and WECC-179 bus system are employed to demonstrate the effectiveness of the proposed OCI-AROP model, and comparisons among the OCI-AROP model and the other models are also given to show its superiority.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

NuLattice: Ab initio computations of atomic nuclei on lattices

Here, we introduce NuLattice, a Python software package for ab initio computations of atomic nuclei on lattices. The computational tools consist of Hartree Fock, the coupled cluster method, the in-medium similarity renormalization group, and full configuration interaction. At present, the employed interactions are from pion-less effective field theory at leading order and consist of two-body and three-body contacts. We present results for light nuclei 2 H, 3,4 He, 8 Be, 12 C, and 16 O. NuLattice algorithms exploit the sparsity and locality of lattice interactions, and as a result computations can be run on laptops.

Rothman, Maxwell [Univ. of Tennessee, Knoxville, T↗
DOE OSTI2022

Insights on sources and formation mechanisms of liquid-bearing clouds over MOSAiC examined from a Lagrangian framework

Understanding Arctic stratiform liquid-bearing cloud life cycles and properly representing these life cycles in models is crucial for evaluations of cloud feedbacks as well as the faithfulness of climate projections for this rapidly warming region. Examination of cloud life cycles typically requires analyses of cloud evolution and origins on short time scales, on the order of hours to several days. Measurements from the recent Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition provide a unique view of the current state of the central Arctic over an annual cycle. Here, we use the MOSAiC radiosonde measurements to detect liquid-bearing cloud layers over full atmospheric columns and to examine the cloud-generating air masses’ properties. We perform 5-day (120 h) back-trajectory calculations for every detected cloud and cluster them using a unique set of variables extracted from these trajectories informed by ERA5 reanalysis data. This clustering method enables us to separate between the air mass source regions such as ice-covered Arctic and midlatitude open water. We find that moisture intrusions into the central Arctic typically result in multilayer liquid-bearing cloud structures and that more than half of multilayer profiles include overlying liquid-bearing clouds originating in different types of air masses. Finally, we conclude that Arctic cloud formation via prolonged radiative cooling of elevated stable subsaturated air masses circulating over the Arctic can occur frequently (up to 20% of detected clouds in the sounding data set) and may lead to a significant impact of ensuing clouds on the surface energy budget, including net surface warming in some cases.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Evaluation of Seismic Artificial Intelligence with Uncertainty

Artificial intelligence has transformed the seismic community with deep learning models (DLMs) that are trained to complete specific tasks within workflows. However, there is still a lack of robust evaluation frameworks for evaluating and comparing DLMs. Here, we address this gap by designing an evaluation framework that jointly incorporates two crucial aspects: performance uncertainty and learning efficiency. To target these aspects, we meticulously construct the training, validation, and test splits using a clustering method tailored to seismic data and enact an expansive training design to segregate performance uncertainty arising from stochastic training processes and random data sampling. The framework’s ability to guard against misleading declarations of model superiority is demonstrated through the evaluation of PhaseNet (Zhu and Beroza, 2018), a popular seismic phase picking DLM, under three training approaches. Our framework helps practitioners choose the best model for their problem and set performance expectations by explicitly analyzing model performance with uncertainty at varying budgets of training data.

58 GEOSCIENCES↗