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At least 271 records · Page 15

All sources NASA NTRS DOE OSTI

DOE OSTI2022

Point Defects in Two-Dimensional Ruddlesden–Popper Perovskites Explored with Ab Initio Calculations

Two-dimensional Ruddlesden–Popper (RP) halide perovskites stand out as excellent layered materials with favorable optoelectronic properties for efficient light-emitting, spintronic, and other spin-related applications. However, properties often determined by defects are not well understood in these perovskite systems. This work investigates the ground state electronic structure of commonly formed defects in a typical RP perovskite structure by density functional theory. Our study reveals that these 2D perovskites generally retain their defect tolerance with limited perturbation of the electronic structure in the case of neutral-type point defects. In contrast, donor/acceptor defects induce deep midgap states, potentially causing harm to the material’s electronic performance. To retain positive intrinsic properties, the halide vacancies and interstitial defects should be avoided. The observed strong electron localization results in trap states and consequently leads to reduced device performance. Finally, this understanding can guide experimental efforts that aim for improved 2D halide perovskite-based device performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Methanol at Water–TiO 2 Interfaces: Free Energies of Water and Methanol Dissociation

Methanol adsorption on TiO 2 surfaces has long been studied due to its role in enhancing photocatalytic hydrogen evolution, yet how it modulates surface chemistry under aqueous conditions remains little understood. Using molecular dynamics with an ab initio-based deep neural network potential, we find that methanol adsorption induces markedly different effects on the aqueous surfaces of anatase and rutile, the two common phases of TiO 2 . In anatase, methanol adsorption significantly enhances water dissociation, which is otherwise rare at the neat water interface. This enhancement arises from an alternative dissociation pathway mediated by surface-bound methoxyl groups. In contrast, methanol adsorption tends to suppress water dissociation on rutile, replacing it with thermodynamically favored methanol dissociation. Overall, methanol adsorption in an aqueous environment alters not only the availability of key reactive intermediates involved in hydrogen evolution but also the hydrogen source, which turns out to be primarily methanol on rutile, whereas both water and methanol are consumed on anatase. These results provide mechanistic insights into the coupled roles of organic adsorbates and water at photocatalytic interfaces, with implications on how methanol enhances the activity of H 2 evolution.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Viscosity in water from first-principles and deep-neural-network simulations

Abstract We report on an extensive study of the viscosity of liquid water at near-ambient conditions, performed within the Green-Kubo theory of linear response and equilibrium ab initio molecular dynamics (AIMD), based on density-functional theory (DFT). In order to cope with the long simulation times necessary to achieve an acceptable statistical accuracy, our ab initio approach is enhanced with deep-neural-network potentials (NNP). This approach is first validated against AIMD results, obtained by using the Perdew–Burke–Ernzerhof (PBE) exchange-correlation functional and paying careful attention to crucial, yet often overlooked, aspects of the statistical data analysis. Then, we train a second NNP to a dataset generated from the Strongly Constrained and Appropriately Normed (SCAN) functional. Once the error resulting from the imperfect prediction of the melting line is offset by referring the simulated temperature to the theoretical melting one, our SCAN predictions of the shear viscosity of water are in very good agreement with experiments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

An interactive machine learning platform for analyzing multi-particle coincidence data from cold target recoil ion momentum spectroscopy

We present SCULPT (Supervised Clustering and Uncovering Latent Patterns with Training), a comprehensive software platform for analyzing tabulated high-dimensional multi-particle coincidence data from Cold Target Recoil Ion Momentum Spectroscopy (COLTRIMS) experiments. The software addresses critical challenges in modern momentum spectroscopy by integrating advanced machine learning techniques with physics-informed analysis in an interactive web-based environment. SCULPT implements uniform manifold approximation and projection for non-linear dimensionality reduction to reveal correlations in high-dimensional data. We also discuss potential extensions to deep autoencoders for feature learning and genetic programming for automated discovery of physically meaningful observables. A novel adaptive confidence scoring system provides quantitative reliability assessments by evaluating user-selected clustering quality metrics with predefined weights that reflect each metric’s robustness. The platform features configurable molecular profiles for different experimental systems, interactive visualization with selection tools, and comprehensive data filtering capabilities. Utilizing a subset of SCULPT’s capabilities, we analyze photo-double-ionization data measured using the COLTRIMS method for three-body dissociation of the D 2 O molecule, revealing distinct fragmentation channels and their correlations with physics parameters. The software’s modular architecture and web-based implementation make it accessible to the broader atomic and molecular physics community, significantly reducing the time required for complex multi-dimensional analyses. This opens the door to finding and isolating rare events exhibiting non-linear correlations on the fly during experimental measurements, which can help steer exploration and improve the efficiency of experiments.

Artificial neural networks↗

DOE OSTI2014

Systematic problems with using dark matter simulations to model stellar halos

The limits of available computing power have forced models for the structure of stellar halos to adopt one or both of the following simplifying assumptions: (1) stellar mass can be 'painted' onto dark matter (DM) particles in progenitor satellites; (2) pure DM simulations that do not form a luminous galaxy can be used. We estimate the magnitude of the systematic errors introduced by these assumptions using a controlled set of stellar halo models where we independently vary whether we look at star particles or painted DM particles, and whether we use a simulation in which a baryonic disk galaxy forms or a matching pure DM simulation that does not form a baryonic disk. We find that the 'painting' simplification reduces the halo concentration and internal structure, predominantly because painted DM particles have different kinematics from star particles even when both are buried deep in the potential well of the satellite. The simplification of using pure DM simulations reduces the concentration further, but increases the internal structure, and results in a more prolate stellar halo. These differences can be a factor of 1.5-7 in concentration (as measured by the half-mass radius) and 2-7 in internal density structure. Given this level of systematic uncertainty, one should be wary of overinterpreting differences between observations and the current generation of stellar halo models based on DM-only simulations when such differences are less than an order of magnitude.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2018

Machine Learning Algorithms for b -Jet Tagging at the ATLAS Experiment

The separation of b -quark initiated jets from those coming from lighter quark flavors ( b -tagging) is a fundamental tool for the ATLAS physics program at the CERN Large Hadron Collider. The most powerful b-tagging algorithms combine information from low-level taggers, exploiting reconstructed track and vertex information, into machine learning classifiers. The potential of modern deep learning techniques is explored using simulated events, and compared to that achievable from more traditional classifiers such as boosted decision trees.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2020

Tilted outer and inner structures in edge-on galaxies?

ABSTRACT Tilted and warped discs inside tilted dark matter haloes are predicted from numerical and semi-analytical studies. In this paper, we use deep imaging to demonstrate the likely existence of tilted outer structures in real galaxies. We consider two SB0 edge-on galaxies, NGC 4469 and NGC 4452, which exhibit apparent tilted outer discs with respect to the inner structure. In NGC 4469, this structure has a boxy shape, inclined by ΔPA ≈ 3° with respect to the inner disc, whereas NGC 4452 harbours a discy outer structure with ΔPA ≈ 6°. In spite of the different shapes, both structures have surface brightness profiles close to exponential and make a large contribution (∼30 per cent) to the total galaxy luminosity. In the case of NGC 4452, we propose that its tilted disc likely originates from a former fast tidal encounter (probably with IC 3381). For NGC 4469, a plausible explanation may also be galaxy harassment, which resulted in a tilted or even a tumbling dark matter halo. A less likely possibility is accretion of gas-rich satellites several Gyr ago. New deep observations may potentially reveal more such galaxies with tilted outer structures, especially in clusters. We also consider galaxies, mentioned in the literature, where a central component (a bar or a bulge) is tilted with respect to the stellar disc. According to our numerical simulations, one of the plausible explanations of such observed ‘tilts’ of the bulge/bar is a projection effect due to a not exactly edge-on orientation of the galaxy coupled with a skew angle of the triaxial bulge/bar.

Mosenkov, Aleksandr V.↗

DOE OSTI2020

Structure of disordered TiO 2 phases from ab initio based deep neural network simulations

Amorphous TiO 2 (a-TiO 2 ) is widely used in many fields, ranging from photo-electrochemistry to bio-engineering, hence detailed knowledge of its atomic structure is of scientific and technological interest. Here we use an ab initio-based deep neural network potential (DP) to simulate large scale atomic models of crystalline and disordered TiO 2 with molecular dynamics. Our DP reproduces the structural properties of all (11) TiO 2 crystalline phases, predicts the densities and structure factors of molten and amorphous TiO 2 with only a few percent deviation from experiments, and describes the pressure dependence of the amorphous structure in agreement with recent observations. Furthermore, it can be extended to model additional structures and compositions and can be thus of great value in the study of TiO 2 -based (nano-)materials.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network

Atomic and molecular properties could be evaluated from the fundamental Schrodinger’s equation and therefore represent different modalities of the same quantum phenomena. Here, we present AIMNet, a modular and chemically inspired deep neural network potential. We used AIMNet with multitarget training to learn multiple modalities of the state of the atom in a molecular system. The resulting model shows on several benchmark datasets state-of-the-art accuracy, comparable to the results of orders of magnitude more expensive DFT methods. It can simultaneously predict several atomic and molecular properties without an increase in the computational cost. With AIMNet, we show a new dimension of transferability: the ability to learn new targets using multimodal information from previous training. The model can learn implicit solvation energy (SMD method) using only a fraction of the original training data and an archive median absolute deviation error of 1.1 kcal/mol compared to experimental solvation free energies in the MNSol database.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2013

Quantum levitation of nanoparticles seen with ultracold neutrons

Analyzing new experiments with ultracold neutrons (UCNs) we show that physical adsorption of nanoparticles/nanodroplets, levitating in high-excited states in a deep and broad potential well formed by van der Waals/Casimir-Polder (vdW/CP) forces results in new effects on a cross-road of the fields of fundamental interactions, neutron, surface and nanoparticle physics. Accounting for the interaction of UCNs with nanoparticles explains a recently discovered intriguing so-called 'small heating' of UCNs in traps. It might be relevant to the striking conflict of the neutron lifetime experiments with smallest reported uncertainties by adding false effects there.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2014

A feasibility study on porting the community land model onto accelerators using OpenACC

As environmental models (such as Accelerated Climate Model for Energy (ACME), Parallel Reactive Flow and Transport Model (PFLOTRAN), Arctic Terrestrial Simulator (ATS), etc.) became more and more complicated, we are facing enormous challenges regarding to porting those applications onto hybrid computing architecture. OpenACC appears as a very promising technology, therefore, we have conducted a feasibility analysis on porting the Community Land Model (CLM), a terrestrial ecosystem model within the Community Earth System Models (CESM)). Specifically, we used automatic function testing platform to extract a small computing kernel out of CLM, then we apply this kernel into the actually CLM dataflow procedure, and investigate the strategy of data parallelization and the benefit of data movement provided by current implementation of OpenACC. Even it is a non-intensive kernel, on a single 16-core computing node, the performance (based on the actual computation time using one GPU) of OpenACC implementation is 2.3 time faster than that of OpenMP implementation using single OpenMP thread, but it is 2.8 times slower than the performance of OpenMP implementation using 16 threads. On multiple nodes, MPI_OpenACC implementation demonstrated very good scalability on up to 128 GPUs on 128 computing nodes. This study also provides useful information for us to look into the potential benefits of “deep copy” capability and “routine” feature of OpenACC standards. In conclusion, we believe that our experience on the environmental model, CLM, can be beneficial to many other scientific research programs who are interested to porting their large scale scientific code using OpenACC onto high-end computers, empowered by hybrid computing architecture.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

TEAMER: FOSWEC Mooring Modeling and Analysis, Post Access Report and Data

Floating oscillating surge wave energy converters (FOSWECs) offer several advantages over bottom-hinged oscillating surge wave energy converters, including large wave potential at deep-water sites with fewer permitting and environmental concerns outside territorial waters. As a team, Stevens Institute of Technology, Virginia Tech and Resolute Marine Energy are designing a 100 kW FOSWEC with DOE support (2020-2021) for the PacWave test site "PacWave". The proposed FOSWEC consists of a floating platform, two pivoting flaps, and an innovative power-take-off (PTO). The distance between the two flaps is around half the typical wavelength, resulting in out-of-phase motion and a reduction in motion of the frame and mooring loads. The overall goal of the project is to design, build, deploy and analyze a 1:2 scale (100-kW annual averaged electrical power output) device with reduced levelized cost of energy (LCOE) and peak-to-average power ratio, through the co-design and control of the PTO, WEC, and floating platform. This submission includes a Post Access Report and data for the project of Mooring Modeling and Analysis for Floating Oscillating Surge Wave Energy Converter that Powers Marine Aquaculture of RFTS2 (request for technical support). The data are used to generate all figures in the Post Access Report. Project was a collaboration between Virginia Tech and the National Renewable Energy Lab, with funding from TEAMER.

16 TIDAL AND WAVE POWER↗

DOE OSTI2026

Daily evapotranspiration changes during heatwaves at 32 NEON sites, 2019-2021

This dataset provides partitioned evapotranspiration (ET, the combined loss of water from soil and plant surfaces) anomalies during heatwave events—soil evaporation (E) and transpiration (T)—for 268 heatwave events across 32 National Ecological Observatory Network (NEON) flux sites in the contiguous United States from 2019–2021. Using an ensemble of four high-frequency turbulence methods (Flux-variance Similarity, Conditional Eddy Covariance [CEC], CEC with Water-Use Efficiency, and Conditional Eddy Accumulation; see Zahn and Bou-Zeid 2024), half-hourly transpiration-to-evapotranspiration (T/ET) ratios were derived from 20 hertz (Hz, cycles per second) eddy covariance measurements of carbon dioxide (CO₂) and water vapor (H₂O) concentrations. The dataset spans six vegetation types including evergreen and deciduous forests, grasslands, cultivated crops, shrublands, and emergent herbaceous wetlands. Data Package Contents: The dataset includes a single CSV (comma-separated values) file containing daily anomalies (deviations from baseline conditions) for transpiration (Delta_T), evaporation (Delta_E), total evapotranspiration (Delta_ET), and T/ET ratio (Delta_T_ET) during each day of identified heatwave events. The file also includes site codes, dates, heatwave event identifiers, and day-of-heatwave indicators. The CSV file can be opened with spreadsheet software (Microsoft Excel, Google Sheets) or programming environments (Python, R, MATLAB). This resource enables researchers to investigate ecosystem-specific responses to thermal extremes, validate land surface model partitioning of ET fluxes, and examine feedbacks between water cycling and surface energy balance during heatwaves. The dataset is particularly valuable for studies linking vegetation hydraulic strategies to climate resilience, as it captures the divergent responses of shallow-rooted versus deep-rooted ecosystems. Potential applications include improving drought early warning systems, informing irrigation management strategies, and advancing our mechanistic understanding of land-atmosphere interactions under extreme heat conditions.

Day of Heatwave↗

DOE OSTI2020

Structure and Stability of Iron Fluoride at High Pressure–Temperature and Implication for a New Reservoir of Fluorine in the Deep Earth

Fluorine (F) is the most abundant halogen in the bulk silicate Earth. F plays an important role in geochemical and biological systems, but its abundance and distribution in the terrestrial mantle are still unclear. Recent studies suggested that F reservoirs in the deep mantle are potentially hosted in terrestrial oxide minerals, especially in aluminous bridgmanite. However, the knowledge about the formation and stability field of fluoride in the Earth’s interior is rare. In this study, we combine in situ laser-heated diamond anvil cell, synchrotron X-ray diffraction, and first-principles structure search to show that a new tetragonal structure of FeF 3 is stable at pressures of 78–130 GPa and temperatures up to ~1900 K. Simulation predicted the tetragonal phase takes a much denser structure due to the rotation of FeF 6 octahedral units. The equations of states of tetragonal FeF3 are determined by experiment and verified by simulation. Our results indicate that FeF 3 can be a potential key phase for storing F in the Earth’s lower mantle and may explain some mantle-derived magma with high F concentration.

58 GEOSCIENCES↗

DOE OSTI2021

Evolution of the Thermodynamic Properties of Clusters of Galaxies out to Redshift of 1.8

The thermodynamic properties of the hot plasma in galaxy clusters retains information on the processes leading to the formation and evolution of the gas in their deep, dark matter potential wells. These processes are dictated not only by gravity but also by gas physics, e.g. AGN feedback and turbulence. Here, we study the thermodynamic properties, e.g. density, temperature, pressure, and entropy, of the most massive and the most distant (z > 1.2) SPT-selected clusters, and compare them with those of the nearby clusters (z < 0.1) to constrain their evolution as a function of time and radius. We find that thermodynamic properties in the outskirts of high redshift clusters are remarkably similar to the low redshift clusters, and their evolution follows the prediction of the self-similar model. Their intrinsic scatter is larger, indicating that the physical properties that lead to the formation and virialization of cluster outskirts show evolving variance. On the other hand, thermodynamic properties in the cluster cores deviates significantly from self-similarity indicating that the processes that regulate the core are already in place in these very high redshift clusters. This result is supported by the unevolving physical scatter of all thermodynamic quantities in cluster cores.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1981

Radio emission in the directions of cD and related galaxies in poor clusters. III - VLA observations at 20 cm

VLA radio maps and optical identifications of a sample of sources in the directions of 21 Yerkes poor cluster fields are presented. The majority of the cluster radio sources are associated with the dominant D or cD galaxies (approximately 70 percent). Our analysis of dominant galaxies in rich and poor clusters indicates that these giant galaxies are much more often radio emitters (approximately 25 percent of cD's are radio active in the poor clusters), have steeper radio spectra, and have simpler radio morphologies (i.e., double or other linear structure) than other less bright ellipticals. A strong continuum of radio properties in cD galaxies is seen from rich to poor clusters. It is speculated that the location of these dominant galaxies at the cluster centers (i.e., at the bottom of a deep, isolated gravitational potential well) is the crucial factor in explaining their multifrequency activity. Galaxy cannibalism and gas infall models as fueling mechanisms for the observed radio and X-ray emission are discussed

Burns, J. O.↗

NASA NTRS1983

Accreting degenerate dwarfs in close binary systems

Advances in the study of cataclysmic variables made during the past few years are reviewed. The classification of cataclysmic binaries and their dynamic properties are summarized. The hard and soft X-ray emission from these objects is discussed, and two alternative accretion geometries for producing this radiation from deep in the potential well of the degenerate dwarf are considered. The ultraviolet and optical spectrum is addressed, including disk emission and contributions from the companion star. Magnetic fields in cataclysmic variables are discussed, and the temporal behavior of these stars is addressed, including periodic modulations associated with orbital motion and rotation as well as flickering and pulsation reflecting the mass transfer process and the dynamics of matter near the surface of the accreting star. The outburst process is considered, including classical novae, recurrent novae, and dwarf novae.

Cordova, F. A.↗

NASA NTRS1993

The Guitar nebula - A bow shock from a slow-spin, high-velocity neutron star

The discovery is reported of a prominent nebula produced by the motion of a high-velocity pulsar, PSR 2224 + 65, through partially neutral gas. The pulsar's transverse speed of over about 800 km/s makes it arguably the fastest known star in the Galaxy and guarantees that it will ultimately escape the Galactic potential well. A deep H-alpha image reveals a bright head and a giant limb-brightened 'body' whose variable width suggests that the ambient interstellar gas has density variations on length scales less than 0.1 pc. Thermalization of shock energy occurs at a rate of about 0.01 times the pulsar's spindown loss rate. These observations provide some insights into the likelihood of finding shocks around other pulsars and the use of nebulae to find high-velocity neutron stars either not acting as pulsars or with their radiation beamed away from the earth.

Cordes, James M.↗