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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 1,441 records · Page 80
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

High-Performance Semiempirical Excited-State Molecular Dynamics Powered by Graphics Processing Units

Here, this Letter introduces excited-state molecular dynamics in PYSEQM, a GPU-accelerated semiempirical quantum chemistry engine implemented in PyTorch. The new module enables Born–Oppenheimer molecular dynamics (BOMD) using configuration-interaction singles and random phase approximation for excited states, allowing long trajectories and large statistical ensembles to be simulated efficiently on a single GPU. We also implement an extended Lagrangian excited-state BOMD (XL-ESMD) scheme that propagates auxiliary electronic variables, enabling relaxed ground and excited-state convergence thresholds without compromising energy conservation. The excited-state BOMD implementation scales smoothly from small chromophores to a nearly 900-atom dendrimer (taking 6.5 s per MD step). PYSEQM also supports batched execution, allowing many geometries or trajectories to be evaluated in a single GPU launch, substantially increasing throughput and making ensemble-based protocols routine. As a demonstration, we compute absorption, emission, and infrared spectra from trajectories propagated on the ground and first excited states. The XL-ESMD scheme yields identical spectra at significantly lower computational cost, establishing the role of extended Lagrangian based dynamics for efficient excited-state BOMD simulations. Beyond raw performance, PYSEQM’s PyTorch foundation provides automatic differentiation for forces, efficient GPU batching, and seamless interfacing with machine learning models. These capabilities position PYSEQM as a practical platform for machine learning-augmented excited-state dynamics and lay the foundation for future data-driven nonadiabatic excited-state dynamics modeling of ultrafast spectroscopic probes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1981

A stochastic-dynamical approach to the study of the natural variability of the climate

A method, suggested by Leith (1975), which employed stochastic-dynamic forecasts obtained from a general circulation model in such a way as to satisfy the definition of climatic noise, was used to validate assumptions accounting for the effects of external influences in estimating the climatic noise. Two assumptions were investigated: (1) that the weather fluctuations can be represented as a Markov process, and (2) that changing external conditions do not influence the atmosphere's statistical properties on short time scales. The general circulation model's simulation of the daily weather fluctuations was generated by performing integrations with prescribed climatological boundary conditions for random initial atmospheric states, with resulting dynamical forecasts providing an ensemble of simulated data for the autoregressive modeling of weather fluctuations. To estimate the climatic noise from the observational data (consisting of hourly values of sea level pressure and surface temperature at 54 U.S. stations for the month of January for the years 1949-1975) use of the short time-scale assumption is made. The simulated and observed data were found not to be consistent with either white noise or a Markov process of weather fluctuations. Good agreement was found between the results of the hypothetical testing of the simulated and the observed surface temperatures; and only partial support was found for the short time-scale assumption, i.e., for sea level pressure.

Straus, D. M.↗
DOE OSTI2022

Megadrought: A Series of Unfortunate La Niña Events?

Megadroughts are multidecadal periods of aridity more persistent than most droughts during the instrumental period. Paleoclimate evidence suggests that megadroughts occur in many parts of the world, including North America, Central America, western Europe, eastern Asia, and northern Africa. It remains unclear to what extent such megadroughts require external forcing or whether they can arise from internal climate variability alone. A novel statistical–dynamical approach is used to evaluate the possibility that such events arise solely as a function of interannual tropical sea surface temperature (SST) variations. A statistical emulator of tropical SST variations is constructed by using an empirical moving-blocks bootstrap approach that randomly samples multiyear sequences of the observational SST record. This approach preserves the power spectrum, seasonal cycle, and spatial pattern of El Niño-Southern Oscillation (ENSO) but removes longer timescale fluctuations embedded in the observational record. These resampled SST anomalies are then used to force an atmospheric model (the Community Atmosphere Model Version 5). As megadroughts emerge in this run, they should, therefore, be solely a consequence of La Niña sequences combined with internal atmospheric variability and persistence driven by soil moisture storage and other land-surface processes. We indeed find that megadroughts in this simulation have an amplitude-duration rate that is generally indistinguishable from the rate documented in paleoclimate records of the western United States. Our findings support the idea that megadroughts may occur randomly when the unforced climate system evolves freely over a sufficiently long period of time, implying that an unforced unusual but statistically plausible series of La Niña events may be sufficient to generate megadrought.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Structural and chemical evolution of the CdS:O window layer during individual CdTe solar cell processing steps

Oxygenated cadmium sulfide (CdS:O) is often used as the n-type window layer in high-performance CdTe heterojunction solar cells. The as-deposited layer prepared by reactive sputtering is XRD amorphous, with a bulk composition of CdS0.8O1.2. Recently it was shown that this layer undergoes significant transformation during device fabrication, but the roles of the individual high temperature processing steps was unclear. In this work high resolution transmission electron microscopy coupled to elemental analysis was used to understand the evolution of the heterojunction region through the individual high temperature fabrication steps of CdTe deposition, CdCl2 activation, and back contact activation. It is found that during CdTe deposition by close spaced sublimation at 600 degrees C the CdS:O film undergoes recrystallization, accompanied by a significant (~30%) reduction in thickness. It is observed that oxygen segregates during this step, forming a bi-layer morphology consisting of nanocrystalline CdS adjacent to the tin oxide contact and an oxygen-rich layer adjacent to the CdTe absorber. This bilayer structure is then lost during the 400 degrees C CdCl2 treatment where the film transforms into a heterogeneous structure with cadmium sulfate clusters distributed randomly throughout the window layer. The thickness of window layer remains essentially unchanged after CdCl2 treatment, but a ~25 nm graded interfacial layer between CdTe and the window region is formed. Finally, the rapid thermal processing step used to activate the back contact was found to have a negligible impact on the structure or composition of the heterojunction region.

14 SOLAR ENERGY↗
DOE OSTI2017

A computational study of yttria-stabilized zirconia: I. Using crystal chemistry to search for the ground state on a glassy energy landscape

Yttria-stabilized zirconia (YSZ), a ZrO 2 -Y 2 O 3 solid solution that contains a large population of oxygen vacancies, is widely used in energy and industrial applications. Past computational studies correctly predicted the anion diffusivity but not the cation diffusivity, which is important for material processing and stability. One of the challenges lies in identifying a plausible configuration akin to the ground state in a glassy landscape. This is unlikely to come from random sampling of even a very large sample space, but the odds are much improved by incorporating packing preferences revealed by a modest sized configurational library established from empirical potential calculations. Ab initio calculations corroborated these preferences, which prove remarkably robust extending to the fifth cation-oxygen shell about 8 Å away. Yet because of frustration there are still rampant violations of packing preferences and charge neutrality in the ground state, and the approach toward it bears a close analogy to glass relaxations. Fast relaxations proceed by fast oxygen movement around cations, while slow relaxations require slow cation diffusion. The latter is necessarily cooperative because of strong coupling imposed by the long-range packing preferences.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Nonparametric, data-based kernel interpolation for particle-tracking simulations and kernel density estimation

Traditional interpolation techniques for particle tracking include binning and convolutional formulas that use pre-determined (i.e., closed-form, parameteric) kernels. In many instances, the particles are introduced as point sources in time and space, so the cloud of particles (either in space or time) is a discrete representation of the Green’s function of an underlying PDE. As such, each particle is a sample from the Green’s function; therefore, each particle should be distributed according to the Green’s function. In short, the kernel of a convolutional interpolation of the particle sample “cloud” should be a replica of the cloud itself. This idea gives rise to an iterative method by which the form of the kernel may be discerned in the process of interpolating the Green’s function. When the Green’s function is a density, this method is broadly applicable to interpolating a kernel density estimate based on random data drawn from a single distribution. We formulate and construct the algorithm and demonstrate its ability to perform kernel density estimation of skewed and/or heavy-tailed data including breakthrough curves.

42 ENGINEERING↗
DOE OSTI2018

A role for myosin VI in retinal pigment epithelium phagocytosis

Highlights: • Quashing myosin VI activity reduces rates of phagosome trafficking in RPE cells. • Trafficked phagosomes in RPE cells undergo two modes of motion. • The more randomly directed mode of phagosome trafficking is myosin VI dependent. • The more directed mode of phagosome trafficking is myosin VI independent. The retinal pigment epithelium (RPE) is a monolayer of pigmented cells adjacent to the choroid coat, the vascular layer of the eye. Among other functions, these cells are responsible for the phagocytosis of rod and cone cell waste shed from the photoreceptor outer segments. We describe here studies to understand the involvement of the motor protein myosin VI in the trafficking of internalized microspheres by a human retinal pigment epithelium primary cell line (ARPE-19). We perturbed the myosin VI-actin interaction by overexpressing a dominant negative myosin VI construct in these cells. We used single particle tracking to characterize the trajectories of internalized fluorescent microspheres. Analysis of the speed of the microspheres' motions revealed that the magnitude of the average speed over short time scales is reduced in the presence of the dominant negative motor. Analysis of the mean-squared displacement of these trajectories demonstrated that trafficking of phagosomes involves two modes of motion, a rapid, more randomly directed motion that is myosin VI dependent and a slower, more directed motion that is independent of the motor. From these data, we posit that this trafficking process involves an interplay between myosin VI motors, which are involved in motion through the actin periphery of the cell, and microtubule motors.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Effect of build geometry on the microstructural development of 316L parts produced by additive manufacturing

Highlights: • Stainless steel ribs were created via direct metal laser sintering to investigate microstructural effect of part design. • EBSD confirmed the presence of large elongated grains in the center of the part, with preferred orientation. • Small grains at the rib surface were found to grow towards the center of the rib with random orientation. • By changing rib thickness and build angle varying texture was observed in the final products. • Large intragranular misorientations were observed in all samples. - Abstract: Additive manufacturing (AM) technology has shown great potential in manufacturing complex products, such as lattice structures without extensive tooling. The focus of this study was to understand the effect of the part geometry on the resulting microstructure. Direct metal laser sintering was used for fabrication of ribs with thicknesses varying between 0.2 and 3.0 mm as well as inclined ribs the with build angles of 30° and 45°. The investigation showed that grains were growing in an epitaxial manner parallel to the building direction. The large elongated grains had a preferential orientation, resulting from the temperature gradient. Additionally, it was found that small grains had formed close to the part surface which were inclined towards the center of the rib. In contrast to the elongated grains, they had a random orientations. The results also indicated that at build angles below 45°, the formed microstructure consisted of the large grains elongated in the building direction. For the used process parameters, the critical part thickness to avoid large elongated grains was found to be about 0.4 mm. These findings allowed us to establish the basics for design rules when it comes to thin wall structures.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Improving E3SM Land Model Photosynthesis Parameterization via Satellite SIF, Machine Learning, and Surrogate Modeling

The parameterization of key photosynthesis parameters is one of the key uncertain sources in modeling ecosystem gross primary productivity (GPP). Solar-induced chlorophyll fluorescence (SIF) offers a good proxy for GPP since it marks the actual process of photosynthesis; while machine learning (ML) provides a robust approach to model the GPP-SIF relationship. Here, we trained the boosted regressing tree (BRT) and the Random Forest ML models with Greenhouse Gases Observing Satellite SIF data and in situ GPP observations from 49 eddy covariance towers. These trained ML GPP-SIF models were fed into the Energy Exascale Earth System Model (E3SM) Land Model (ELM) to generate ELM-simulated global SIF estimates, which were then benchmarked against satellite SIF observations with a surrogate modeling approach. Our results indicated good modeling performance of the ML-based GPP-SIF relationship. The ELM model when fed with the ML GPP-SIF models also can well predict the spatial-temporal variations in SIF. We also found high model accuracy for the surrogate modeling. Model parameter sensitivity analysis suggested that the fraction of leaf nitrogen in RuBisCO (flnr) is the most sensitive parameter to the SIF; other sensitive parameters include the Ball-Berry stomatal conductance slope (mbbopt) and the vcmax entropy (vcmaxse). The posterior uncertainty in simulated GPP was greatly reduced after benchmarking, and the model produced improved spatial patterns of mean GPP relative to FLUXCOM GPP. Our integrated approach provides a new avenue for improving land models and using remote-sensing SIF, which can be further improved in the future with more ground- and satellite-based observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Intrinsic nonlocality of spin- and polarization-resolved probabilities in strong-field quantum electrodynamics

Spin and polarization are central to precision tests of fundamental physics and for interpreting radiation from astrophysical sources and ultraintense laser-matter experiments. Here, focusing on the fundamental process of nonlinear Compton scattering, we demonstrate that a key assumption underlying current strong-field quantum electrodynamics models, i.e., that emission can be treated as an instantaneous random event sampled from a local differential rate, is inconsistent once emission angles, electron spin, and/or photon polarization are resolved. Namely, even in strictly constant and uniform fields , the resulting fully differential distribution is sign indefinite, yielding negative inferred probabilities. The physical reason is that the photon emission probability builds up over a finite length of the electron trajectory, the formation region, during which the electron direction changes by roughly the same small angle that defines the radiation cone. Therefore, we put forward a new method where we integrate over this formation region analytically to obtain a physically consistent electron spin and photon polarization model. We show that the implementation of our model is compatible with existing Monte Carlo and particle-in-cell workflows. Simulations of a GeV-class electron-laser collision accessible at current petawatt facilities and of emission in a pulsarlike magnetic field are shown to reveal spin and polarization patterns that differ even qualitatively from state-of-the-art local models. In particular, our new model predicts substantial angle-dependent circular photon polarization where the well-known collinear-emission approach yields none, and a pronounced helicity bias in the recoiling electrons absent from current predictions. These findings have direct implications for upcoming strong-field QED experiments and for interpreting polarized radiation from extreme astrophysical environments.

astrophysical electromagnetic fields↗
DOE OSTI2017

GPU-based parallel algorithm for generating massive scale-free networks using the preferential attachment model

A novel parallel algorithm is presented for generating random scale-free networks using the preferential-attachment model. The algorithm, named cuPPA, is custom-designed for single instruction multiple data (SIMD) style of parallel processing supported by modern processors such as graphical processing units (GPUs). To the best of our knowledge, our algorithm is the first to exploit GPUs, and also the fastest implementation available today, to generate scale-free networks using the preferential attachment model. A detailed performance study is presented to understand the scalability and runtime characteristics of the cuPPA algorithm. In one of the best cases, when executed on an NVidia GeForce 1080 GPU, cuPPA generates a scale-free network of two billion edges in less than 3 seconds.

Alam, Maksudul↗
DOE OSTI2017

Role of polarization interaction in photoionization of negative ions of alkali metals

Photoionization processes of negative ions of sodium, potassium, and rubidium were investigated. Calculations of the dependence of the photoionization cross section on the photon energy were performed within the Random Phase Approximation with Exchange for outer subshells (RPAE) and within the Generalized Random Phase Approximation with Exchange (GRPAE) for inner subshells. The latter theoretical approach includes both many-electron correlations and core rearrangement due to escape of one of the electrons from the system. The results of calculations for the negative sodium ion were found to be in good agreement with experimental data. Better agreement was achieved by allowing dynamical polarization of the electron core. It manifests itself as a many-electron response to variation of the external electromagnetic field, which results from the excitation of the many-electron system. Detailed study of the main mechanisms determining the cross section dependence profile was carried out. These mechanisms are the inter- and intrachannel correlations acting as a many-electron response to the external field, the electron core rearrangement, and the dynamic polarization. Besides sodium and potassium ions, photoionization of rubidium ion was investigated. A new method accounting for polarization corrections to optical transition amplitudes based on combination of the Dyson equation and RPAE is proposed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Simulated Contrast of Two Dislocations

A three-dimensional Monte Carlo simulation algorithm is used to study the contrast of two dislocations perpendicular to the irradiated surface of an n-doped silicon sample in the electron beam induced current mode. The dislocations are positioned in the irradiation trajectory, and each of both is considered as a cylinder where the minority carrier diffusion length varies abruptly from a low inside dislocations up to a high value outside dislocations. The EBIC contrast was obtained by simulating the random diffusion of carriers generated at point-like sources randomly distributed within the generation volume. Results are analyzed on the basis of change in the generation volume in the bulk of the sample and of carrier trapping process inside dislocations. The EBIC contrast increases with the increase of the electron beam energy. It also increases when the minority diffusion length inside dislocations, or their separating distance decreases.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Machine Learning Approach for Spatiotemporal Multivariate Optimization of Environmental Monitoring Sensor Locations

Abstract Long-term environmental monitoring is critical for managing the soil and groundwater at contaminated sites. Recent improvements in state-of-the-art sensor technology, communication networks, and artificial intelligence have created opportunities to modernize this monitoring activity for automated, fast, robust, and predictive monitoring. In such modernization, it is required that sensor locations be optimized to capture the spatiotemporal dynamics of all monitoring variables as well as to make it cost-effective. The legacy monitoring datasets of the target area are important to perform this optimization. In this study, we have developed a machine-learning approach to optimize sensor locations for soil and groundwater monitoring based on ensemble supervised learning and majority voting. For spatial optimization, Gaussian process regression (GPR) is used for spatial interpolation, while the majority voting is applied to accommodate the multivariate temporal dimension. Results show that the algorithms significantly outperform the random selection of the sensor locations for predictive spatiotemporal interpolation. While the method has been applied to a four-dimensional dataset (with two-dimensional space, time, and multiple contaminants), we anticipate that it can be generalizable to higher-dimensional datasets for environmental monitoring sensor location optimization.

Siddiquee, Masudur R.↗
DOE OSTI2017

Generating Billion-Edge Scale-Free Networks in Seconds: Performance Study of a Novel GPU-based Preferential Attachment Model

A novel parallel algorithm is presented for generating random scale-free networks using the preferential-attachment model. The algorithm, named cuPPA, is custom-designed for single instruction multiple data (SIMD) style of parallel processing supported by modern processors such as graphical processing units (GPUs). To the best of our knowledge, our algorithm is the first to exploit GPUs, and also the fastest implementation available today, to generate scale free networks using the preferential attachment model. A detailed performance study is presented to understand the scalability and runtime characteristics of the cuPPA algorithm. In one of the best cases, when executed on an NVidia GeForce 1080 GPU, cuPPA generates a scale free network of a billion edges in less than 2 seconds.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Efficiently improving the performance of noisy quantum computers

Using near-term quantum computers to achieve a quantum advantage requires efficient strategies to improve the performance of the noisy quantum devices presently available. We develop and experimentally validate two efficient error mitigation protocols named "Noiseless Output Extrapolation" and "Pauli Error Cancellation" that can drastically enhance the performance of quantum circuits composed of noisy cycles of gates. By combining popular mitigation strategies such as probabilistic error cancellation and noise amplification with efficient noise reconstruction methods, our protocols can mitigate a wide range of noise processes that do not satisfy the assumptions underlying existing mitigation protocols, including non-local and gate-dependent processes. We test our protocols on a four-qubit superconducting processor at the Advanced Quantum Testbed. We observe significant improvements in the performance of both structured and random circuits, with up to 86 % improvement in variation distance over the unmitigated outputs. Our experiments demonstrate the effectiveness of our protocols, as well as their practicality for current hardware platforms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

The Evolution of Volatile Memory Forensics

The collection and analysis of volatile memory is a vibrant area of research in the cybersecurity community. The ever-evolving and growing threat landscape is trending towards fileless malware, which avoids traditional detection but can be found by examining a system’s random access memory (RAM). Additionally, volatile memory analysis offers great insight into other malicious vectors. It contains fragments of encrypted files’ contents, as well as lists of running processes, imported modules, and network connections, all of which are difficult or impossible to extract from the file system. For these compelling reasons, recent research efforts have focused on the collection of memory snapshots and methods to analyze them for the presence of malware. However, to the best of our knowledge, no current reviews or surveys exist that systematize the research on both memory acquisition and analysis. We fill that gap with this novel survey by exploring the state-of-the-art tools and techniques for volatile memory acquisition and analysis for malware identification. For memory acquisition methods, we explore the trade-offs many techniques make between snapshot quality, performance overhead, and security. For memory analysis, we examined the traditional forensic methods used, including signature-based methods, dynamic methods performed in a sandbox environment, as well as machine learning-based approaches. We summarize the currently available tools, and suggest areas for more research.

Nyholm, Hannah↗
NASA NTRS1979

Semiconductor technology program. Progress briefs

The current status of NBS work on measurement technology for semiconductor materials, process control, and devices is reported. Results of both in-house and contract research are covered. Highlighted activities include modeling of diffusion processes, analysis of model spreading resistance data, and studies of resonance ionization spectroscopy, resistivity-dopant density relationships in p-type silicon, deep level measurements, photoresist sensitometry, random fault measurements, power MOSFET thermal characteristics, power transistor switching characteristics, and gross leak testing. New and selected on-going projects are described. Compilations of recent publications and publications in press are included.

Bullis, W. M.↗