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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Design Rules of the Mixing Phase and Impacts on Device Performance in High-Efficiency Organic Photovoltaics

In nonfullerene acceptor- (NFA-) based solar cells, the exciton splitting takes place at both domain interface and donor/acceptor mixture, which brings in the state of mixing phase into focus. The energetics and morphology are key parameters dictating the charge generation, diffusion, and recombination. It is revealed that tailoringthe electronic properties of the mixing region by doping with larger-bandgap components could reduce the density of state but elevate the filling state level, leading to improved open-circuit voltage ( V OC ) and reduced recombination. The monomolecular and bimolecular recombinations are shown to be intercorrelated, which show a Gaussian-like relationship with V OC and linear relationship with short-circuit current density ( J SC ) and fill factor (FF). The kinetics of hole transfer and exciton diffusion scale with J SC similarly, indicating the carrier generation in mixing region and crystalline domain are equally important. From the morphology perspective, the crystalline order could contribute to V OC improvement, and the fibrillar structure strongly affects the FF. These observations highlight the importance of the mixing region and its connection with crystalline domains and point out the design rules to optimize the mixing phase structure, which is an effective approach to further improve device performance.

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

Debiasing with Diffusion: Probabilistic Reconstruction of Dark Matter Fields from Galaxies with CAMELS

Abstract Galaxies are biased tracers of the underlying cosmic web, which is dominated by dark matter (DM) components that cannot be directly observed. Galaxy formation simulations can be used to study the relationship between DM density fields and galaxy distributions. However, this relationship can be sensitive to assumptions in cosmology and astrophysical processes embedded in galaxy formation models, which remain uncertain in many aspects. In this work, we develop a diffusion generative model to reconstruct DM fields from galaxies. The diffusion model is trained on the CAMELS simulation suite that contains thousands of state-of-the-art galaxy formation simulations with varying cosmological parameters and subgrid astrophysics. We demonstrate that the diffusion model can predict the unbiased posterior distribution of the underlying DM fields from the given stellar density fields while being able to marginalize over uncertainties in cosmological and astrophysical models. Interestingly, the model generalizes to simulation volumes ≈500 times larger than those it was trained on and across different galaxy formation models. The code for reproducing these results can be found athttps://github.com/victoriaono/variational-diffusion-cdm✎.

Astronomy & Astrophysics↗
DOE OSTI2026

Regularizing INR with Diffusion Prior for Self-Supervised 3D Reconstruction OF Neutron Computed Tomography Data

Recently, generative diffusion priors have made huge strides as inverse problem solvers, including the ability to be adapted for inference on out-of-distribution data. Concurrently, implicit neural representations (INRs) have emerged as fast and lightweight inverse imaging solvers that are amenable to hybrid approaches that combine learned priors with traditional inverse problem formulations. In this paper, we present a diffusive computed tomography (CT) inversion framework for regularizing INRs called Diffusive INR (DINR), designed to enable high-quality reconstruction from sparse-view neutron CT. Pretrained purely on synthetic data, DINR is evaluated on simulated and experimentally obtained observations of concrete microstructures, where traditional reconstruction methods suffer substantial degradation when the number of views is reduced. Our approach delivers superior performance, reduces reconstruction artifacts, and achieves gains in PSNR and SSIM, enabling accurate micro-structural characterization even under extreme data limitations compared to state-of-the-art sparse-view reconstruction techniques.

Hossain, Maliha [ORNL]↗
DOE OSTI2026

Model Parameter Development for Complex Materials: Species-Specific Diffusion Barriers in 316 Stainless Steel from Systematic DFT Calculations

Vacancy-mediated diffusion barriers in 316 stainless steel have been systematically calculated using density functional theory to provide essential parameters for mesoscale microstructure evolution models. A statistical sampling approach employing 210 nudged elastic band calculations across multiple special quasi-random structures captures the effects of local chemical environments in this concentrated alloy. The computational methodology addresses challenges specific to chemically disordered systems, including proper magnetic treatment throughout multi-step calculations and validation against experimental structural properties. The calculated activation barriers reveal clear species-dependent diffusion behavior with the hierarchy Ni >> Fe ˜ Cr >> Mo. Nickel exhibits the highest barriers (0.74–1.31 eV, mean 1.045 eV), confirming its role as the slowest-diffusing major component. Iron and chromium show similar moderate barriers averaging 0.587 eV and 0.522 eV, respectively. Remarkably, molybdenum demonstrates exceptionally low barriers (0.12–0.28 eV, mean 0.194 eV), suggesting much higher mobility than previously recognized and potentially significant implications for precipitation kinetics and microstructure evolution. The barrier ranges remain consistent across different 316 SS compositions, supporting parameter transferability for modeling applications. The overall mean barrier of 0.64 eV provides a practical approximation for phase field simulations, while species-specific values enable detailed treatments of diffusion-controlled processes. This systematic approach establishes a validated framework for generating diffusion parameters in other concentrated alloys where experimental data are limited, while providing the first systematic set of species-specific barriers for predictive modeling of 316 stainless steel microstructure evolution.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Synthetic Scientific Image Generation with VAE, GAN, and Diffusion Model Architectures

Generative AI (genAI) has emerged as a powerful tool for synthesizing diverse and complex image data, offering new possibilities for scientific imaging applications. This review presents a comprehensive comparative analysis of leading generative architectures, ranging from Variational Autoencoders (VAEs) to Generative Adversarial Networks (GANs) on through to Diffusion Models, in the context of scientific image synthesis. We examine each model's foundational principles, recent architectural advancements, and practical trade-offs. Our evaluation, conducted on domain-specific datasets including microCT scans of rocks and composite fibers, as well as high-resolution images of plant roots, integrates both quantitative metrics (SSIM, LPIPS, FID, CLIPScore) and expert-driven qualitative assessments. Results show that GANs, particularly StyleGAN, produce images with high perceptual quality and structural coherence. Diffusion-based models for inpainting and image variation, such as DALL-E 2, delivered high realism and semantic alignment but generally struggled in balancing visual fidelity with scientific accuracy. Importantly, our findings reveal limitations of standard quantitative metrics in capturing scientific relevance, underscoring the need for domain-expert validation. We conclude by discussing key challenges such as model interpretability, computational cost, and verification protocols, and discuss future directions where generative AI can drive innovation in data augmentation, simulation, and hypothesis generation in scientific research.

Generative Adversarial Networks↗
DOE OSTI2024

CaloScore v2: single-shot calorimeter shower simulation with diffusion models

Diffusion generative models are promising alternatives for fast surrogate models, producing high-fidelity physics simulations. However, the generation time often requires an expensive denoising process with hundreds of function evaluations, restricting the current applicability of these models in a realistic setting. In this work, we report updates on the CaloScorearchitecture, detailing the changes in the diffusion process, which produces higher quality samples, and the use of progressive distillation, resulting in a diffusion model capable of generating new samples with a single function evaluation. Here we demonstrate these improvements using the Calorimeter Simulation Challenge 2022 dataset.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

A mass-transfer particle-tracking method for simulating transport with discontinuous diffusion coefficients

The problem of a spatially discontinuous diffusion coefficient (D(x)) is one that may be encountered in hydrogeologic systems due to natural geological features or as a consequence of numerical discretization of flow properties. To date, mass-transfer particle-tracking (MTPT) methods, a family of Lagrangian methods in which diffusion is jointly simulated by random walk and diffusive mass transfers, have been unable to solve this problem. This manuscript presents a new mass-transfer (MT) algorithm that enables MTPT methods to accurately solve the problem of discontinuous D(x). To achieve this, we derive a semi-analytical solution to the discontinuous D(x) problem by employing a predictor-corrector approach, and we use this semi-analytical solution as the weighting function in a reformulated MT algorithm. Furthermore, this semi-analytical solution is generalized for cases with multiple 1D interfaces as well as for 2D cases, including a 2 × 2 tiling of 4 subdomains that corresponds to a numerically-generated diffusion field. The solutions generated by this new mass-transfer algorithm closely agree with an analytical 1D solution or, in more complicated cases, trusted numerical results, demonstrating the success of our proposed approach.

42 ENGINEERING↗
DOE OSTI2014

The effects of applied current on one-dimensional interdiffusion between copper and nickel in spark plasma sintering

Spark plasma sintering (SPS) is a powder metallurgy technique that employs the use of fast sintering kinetics to produce final consolidated components in a matter of minutes. In order to use blended powders in SPS to obtain fully alloyed parts, diffusion during sintering must be understood. An investigation into the effects of current on the diffusion of copper and nickel was performed using SPS. Bulk specimens were used to generate diffusion couples in SPS in alternating orientations with respect to the direction of the current. Control samples were produced using a horizontal insertion vacuum furnace. Experiments were performed at temperatures between 850°C and 1000°C for 3 h. Concentration profiles were obtained by the use of both energy-dispersive spectroscopy and a Monte Carlo simulated correction curve. Diffusion coefficients and activation energies were calculated for samples produced by SPS and annealing without current. It was shown that, at temperatures near 0.9 T{sub m}, the application of current in SPS inhibits diffusion between copper and nickel due to the re-orientation of electrons caused by the loss of ferromagnetism in nickel. Activation energy for diffusion is, however, decreased due to the temperature gradients arising from the difference in resistivity between the two species.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Resolving turbulent magnetohydrodynamics: a hybrid operator-diffusion framework

We present a hybrid machine learning framework that combines physics-informed neural operators (PINOs) with score-based generative diffusion models to simulate the full spatio-temporal evolution of two-dimensional, incompressible, resistive magnetohydrodynamic turbulence across a broad range of Reynolds numbers (Re). The framework leverages the equation-constrained generalization capabilities of PINOs to predict coherent, low-frequency dynamics, while a conditional diffusion model stochastically corrects high-frequency residuals, enabling accurate modeling of fully developed turbulence. Trained on a comprehensive ensemble of high-fidelity simulations with Re ϵ {100, 250, 500, 750, 1000, 3000, 10000}, the approach achieves state-of-the-art accuracy in regimes previously inaccessible to deterministic surrogates. At Re = 1000 and 3000, the model faithfully reconstructs the full spectral energy distributions of both velocity and magnetic fields late into the simulation, capturing non-Gaussian statistics, intermittent structures, and cross-field correlations with high fidelity. At extreme turbulence levels (Re = 10 000), it remains the first surrogate capable of recovering the high-wavenumber evolution of the magnetic field, preserving large-scale morphology and enabling statistically meaningful predictions.

Diffusion-Integrated Neural Operators↗
DOE OSTI2023

Modeling the Charge and Exciton Distributions in Phosphorescent White Organic Light-Emitting Diodes

A comprehensive numerical model is developed to quantitatively analyze the charge and exciton distributions in phosphorescent white organic light emitting diodes (WOLEDs). The model encompasses bipolar charge drift-diffusion transport with doping-dependent charge mobilities, field-dependent charge hopping across multiple interfaces, exciton generation, diffusion, energy transfer, radiative decay, and non-radiative exciton annihilation. The model is validated by direct measurement of the exciton distribution in monochromatic phosphorescent OLEDs. It is then applied to complex, multilayer WOLEDs where it is used to accurately predict their intensity and current-dependent spectral shifts. The magnitudes of different exciton loss channels in WOLEDs are calculated and quantitatively compared. This model provides an alternative for the many experimental iterations commonly used to optimize the color balance of complex OLEDs. In this context, the model inspired the addition of a hole blocking layer in the WOLED to balance the charge conduction and reduce spectral shifts – a design that is verified by experiment.

42 ENGINEERING↗
DOE OSTI2015

Spatial Resolution Versus Data Acquisition Efficiency in Mapping an Inhomogeneous System with Species Diffusion

Traditionally, spatially-resolved photoluminescence (PL) has been performed using a point-by-point scan mode with both excitation and detection occurring at the same spatial location. But with the availability of high quality detector arrays like CCDs, an imaging mode has become popular for performing spatially-resolved PL. By illuminating the entire area of interest and collecting the data simultaneously from all spatial locations, the measurement efficiency can be greatly improved. However, this new approach has proceeded under the implicit assumption of comparable spatial resolution. We show here that when carrier diffusion is present, the spatial resolution can actually differ substantially between the two modes, with the less efficient scan mode being far superior. We apply both techniques in investigation of defects in a GaAs epilayer – where isolated singlet and doublet dislocations can be identified. A superposition principle is developed for solving the diffusion equation to extract the intrinsic carrier diffusion length, which can be applied to a system with arbitrarily distributed defects. The understanding derived from this work is significant for a broad range of problems in physics and beyond (for instance biology) – whenever the dynamics of generation, diffusion, and annihilation of species can be probed with either measurement mode.

14 SOLAR ENERGY↗
DOE OSTI2020

Defect cluster and nonequilibrium gas bubble associated growth in irradiated UMo fuels – a cluster dynamics and phase field model

Irradiation examination shows that gas bubble swelling kinetics is much faster after irradiation-induced recrystallization than that prior recrystallization in UMo fuels. It implies that gas bubbles in coarse grains and small recrystallized grains have different growth behavior. In this work, a phase-field model of gas bubble evolution integrating microstructure dependent cluster dynamics has been developed, for the first time, to study the gas bubble swelling behavior in the recrystallization zone of UMo fuels. Generation, diffusion, reaction, sink, emission and clustering of vacancies and interstitials are described by the cluster dynamics model while a phase-field model is used to describe the evolution of non-equilibrium gas bubbles including nucleation and growth. With the coupled model, the effect of defect generation rate, clustering rate, interstitial emission and sink rates on grain boundaries on the gas bubble evolution are systematically simulated. A set of model parameters (defect generation rate, clustering rate, interstitial emission and sink rates) is determined by comparing measured and simulated gas bubble swelling kinetics. The results demonstrate that interstitial clustering is one of the important physical mechanisms which results in a fast gas bubble swelling kinetics in the recrystallization zone. The developed model can also be extended to study the associated growth of defect and second phase precipitates often observed in irradiated materials.

Hu, Shenyang↗
DOE OSTI2014

Modeling MHD accretion-ejection: episodic ejections of jets triggered by a mean-field disk dynamo

We present MHD simulations exploring the launching, acceleration, and collimation of jets and disk winds. The evolution of the disk structure is consistently taken into account. Extending our earlier studies, we now consider the self-generation of the magnetic field by an α{sup 2}Ω mean-field dynamo. The disk magnetization remains on a rather low level, which helps to evolve the simulations for T > 10, 000 dynamical time steps on a domain extending 1500 inner disk radii. We find the magnetic field of the inner disk to be similar to the commonly found open field structure, favoring magneto-centrifugal launching. The outer disk field is highly inclined and predominantly radial. Here, differential rotation induces a strong toroidal component, which plays a key role in outflow launching. These outflows from the outer disk are slower, denser, and less collimated. If the dynamo action is not quenched, magnetic flux is continuously generated, diffuses outward through the disk, and fills the entire disk. We have invented a toy model triggering a time-dependent mean-field dynamo. The duty cycles of this dynamo lead to episodic ejections on similar timescales. When the dynamo is suppressed as the magnetization falls below a critical value, the generation of the outflows and also accretion is inhibited. The general result is that we can steer episodic ejection and large-scale jet knots by a disk-intrinsic dynamo that is time-dependent and regenerates the jet-launching magnetic field.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Recovering position-dependent diffusion from biased molecular dynamics simulations

All atom molecular dynamics (MD) models provide valuable insight into the dynamics of biophysical systems, but are limited in size or length by the high computational demands. The latter can be reduced by simulating long term diffusive dynamics (also known as Langevin dynamics or Brownian motion) of the most interesting and important user-defined parts of the studied system, termed collective variables (colvars). A few hundred nanosecond-long biased MD trajectory can therefore be extended to millisecond lengths in the colvars subspace at a very small additional computational cost. In this work, we develop a method for determining multidimensional anisotropic position- and timescale-dependent diffusion coefficients (D) by analysing the changes of colvars in an existing MD trajectory. As a test case, we obtained D for dihedral angles of the alanine dipeptide. An open source Mathematica{sup ®} package, capable of determining and visualizing D in one or two dimensions, is available at https://github.com/lbf-ijs/DiffusiveDynamics . Given known free energy and D, the package can also generate diffusive trajectories.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Modelling of illuminated current–voltage characteristics to evaluate leakage currents in long wavelength infrared mercury cadmium telluride photovoltaic detectors

The current–voltage characteristics of long wavelength mercury cadmium telluride infrared detectors have been studied using a recently suggested method for modelling of illuminated photovoltaic detectors. Diodes fabricated on in-house grown arsenic and vacancy doped epitaxial layers were evaluated for their leakage currents. The thermal diffusion, generation–recombination (g-r), and ohmic currents were found as principal components of diode current besides a component of photocurrent due to illumination. In addition, both types of diodes exhibited an excess current component whose growth with the applied bias voltage did not match the expected growth of trap-assisted-tunnelling current. Instead, it was found to be the best described by an exponential function of the type, I{sub excess} = I{sub r0} + K{sub 1} exp (K{sub 2} V), where I{sub r0}, K{sub 1}, and K{sub 2} are fitting parameters and V is the applied bias voltage. A study of the temperature dependence of the diode current components and the excess current provided the useful clues about the source of origin of excess current. It was found that the excess current in diodes fabricated on arsenic doped epitaxial layers has its origin in the source of ohmic shunt currents. Whereas, the source of excess current in diodes fabricated on vacancy doped epitaxial layers appeared to be the avalanche multiplication of photocurrent. The difference in the behaviour of two types of diodes has been attributed to the difference in the quality of epitaxial layers.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Electron-Beam-Related Studies of Halide Perovskites: Challenges and Opportunities

Electron beam microscopy and related characterization techniques play an important role in revealing the microstructural, morphological, physical, and chemical information of halide perovskites and their impact on associated optoelectronic devices. However, electron beam irradiation usually causes damage to these beam-sensitive materials, negatively impacting their device performance, and complicating this interpretation. Here, the electron microscopy and spectroscopy techniques are reviewed that are crucial for the understanding of the crystallization and microstructure of halide perovskites. In addition, special attention is paid to assessing and mitigating the electron beam-induced damage caused by these techniques. Since the halide perovskites are fragile, a protocol involving delicate control of both electron beam dose and dose rate, coupled with careful data analysis, is key to enable the acquisition of reliable structural and compositional information such as atomic-resolution images, chemical elemental mapping and electron diffraction patterns. Limiting the electron beam dose is critical parameter enabling the characterization of various halide perovskites. Novel methods to unveil the mechanisms of device operation, including charge carrier generation, diffusion, and extraction are presented in scanning electron microscopy studies combined with electron-beam-induced current and cathodoluminescence mapping. Future opportunities for electron-beam-related characterizations of halide perovskites are also discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2018

The Interface Influence in TiN/SiN{sub x} Multilayer Nanocomposite Under Irradiation

The paper focuses on studying the kinetics of radiation-induced point defects formed in TiN/SiN{sub x} multilayer nanocomposites with account of their generation, diffusion recombination, and the influence of sinks functioning as interfaces. In order to describe the kinetics in nanocrystalline TiN and amorphous SiN{sub x} phases, a finite-difference method is used to solve the system of balance kinetic equations for absolute defect concentrations depending on the spatiotemporal variables. A model of the disclination-dislocation interface structure is used to study the absorption of radiation-induced point defects on the boundaries in created stress fields. It is shown that the interface effectively absorbs point defects in these phases of TiN/SiN{sub x} multilayer nanocomposite, thereby reducing their amount within the space between phases. This behavior of point defects partially explains a mechanism of the radiation resistance in this type of nanocomposites.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2011

Phase-field simulation of irradiated metals Part II : Gas bubble kinetics.

The phase-field model developed in Part I of this work is expanded to include fission gas generation, diffusion, and segregation within bubbles nucleated both homogeneously and heterogeneously along grain boundaries. Illustrative results are presented that characterize bubble growth and shrinkage, as well as the bubble density, size and nucleation rate as a function of the irradiation conditions. Finally, intergranular bubble characteristics such as shape, pinning energy and bubble density are investigated.

36 MATERIALS SCIENCE↗