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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Search for the jet-induced diffusion wake in the quark-gluon plasma via measurements of jet-track correlations in photon-jet events in Pb+Pb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV with the ATLAS detector

This paper presents a measurement of jet-track correlations in photon-jet events, using 1.72 nb −1 of Pb+Pb data at $\sqrt{s_{NN}}$ = 5.02TeV recorded with the ATLAS detector at the LHC. Events with energetic photon-jet pairs are selected, where the photon and jet are approximately back-to-back in azimuth. The angular correlation between jets and charged-particle tracks with transverse momentum (𝑝 T ) in the range 0.5–2.0 GeV in the hemisphere opposite to the jet, |Δ⁢𝜙(jet,track)|>𝜋/2, is measured as a function of their relative pseudorapidity difference, |Δ⁢𝜂(jet,track)|. In central Pb+Pb collisions, these correlations are predicted to be sensitive to the diffusion wake in the quark-gluon plasma resulting from the lost energy of high-𝑝T partons traversing the plasma, with a characteristic modification as a function of |Δ⁢𝜂(jet,track)|. The correlations are examined with different selections on the jet-to-photon 𝑝 T ratio to select events with different degrees of energy loss. No diffusion wake signal is observed within the current sensitivity and upper limits at 95% confidence level on the diffusion wake amplitude are reported.

heavy flavor physics↗
DOE OSTI2023

Denoising diffusion models with geometry adaptation for high fidelity calorimeter simulation

Simulation is crucial for all aspects of collider data analysis, but the available computing budget in the High Luminosity LHC era will be severely constrained. Generative machine learning models may act as surrogates to replace physics-based full simulation of particle detectors, and diffusion models have recently emerged as the state of the art for other generative tasks. Here, we introduce CaloDiffusion, a denoising diffusion model trained on the public CaloChallenge datasets to generate calorimeter showers. Our algorithm employs 3D cylindrical convolutions, which take advantage of symmetries of the underlying data representation. To handle irregular detector geometries, we augment the diffusion model with a new geometry latent mapping (GLaM) layer to learn forward and reverse transformations to a regular geometry that is suitable for cylindrical convolutions. The showers generated by our approach are nearly indistinguishable from the full simulation, as measured by several different metrics.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Diffusion model approach to simulating electron-proton scattering events

Generative artificial intelligence is a fast-growing area of research offering various avenues for exploration in high-energy nuclear physics. In this work, we explore the use of generative models for simulating electron-proton collisions relevant to experiments like the Continuous Electron Beam Accelerator Facility and the future Electron-Ion Collider (EIC). These experiments play a critical role in advancing our understanding of nucleons and nuclei in terms of quark and gluon degrees of freedom. The use of generative models for simulating collider events faces several challenges such as the sparsity of the data, the presence of global or eventwide constraints, and steeply falling particle distributions. In this work, we focus on the implementation of diffusion models for the simulation of electron-proton scattering events at EIC energies. Our results demonstrate that diffusion models can reproduce relevant observables such as momentum distributions and correlations of particles, momentum sum rules, and the leading electron kinematics, all of which are of particular interest in electron-proton collisions. Although the sampling process is relatively slow compared to other machine-learning architectures, we find diffusion models can generate high-quality samples. We foresee various applications of our work including inference for nuclear structure, interpretable generative machine learning, and searches of physics beyond the Standard Model. Published by the American Physical Society 2024

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Diffuse boosted cosmic neutrino background

Energetic cosmic rays scatter off the cosmic neutrino background throughout the history of the Universe, yielding a diffuse flux of cosmic relic neutrinos boosted to high energies. We calculate this flux under different assumptions of the cosmic-ray flux spectral slope and redshift evolution. The nonobservation of the diffuse flux of boosted relic neutrinos with current high-energy neutrino experiments already excludes an average cosmic neutrino background overdensity larger than ∼10 4 over cosmological distances. We discuss the future detectability of the diffuse flux of boosted relic neutrinos in light of neutrino overdensity estimates and cosmogenic neutrino backgrounds.

Astronomy & Astrophysics↗
DOE OSTI2020

Recovering Gardner restacking with purely diffusive operations

The maximum particle kinetic energy that can be extracted from an initial six-dimensional phase space distribution motivates the concept of free or available energy. The free energy depends on the allowed operations that can be performed. A key concept underlying the theoretical treatment of plasmas is the Gardner free energy, where the exchange of the contents of equal phase volumes is allowed. Further, a second free energy concept is the diffusive free energy, in which the contents of volumes are instead averaged. For any finite discretization of phase space, the diffusive free energy is known to be less than the Gardner free energy. However, despite the apparent fundamental differences between these free energies, it is demonstrated here that the Gardner free energy may be recovered from the continuous limit of the diffusive free energy, leading to the surprise that macroscopic phase-space conservation can be achieved by ostensibly entropy-producing microscopic operations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Discrete generative diffusion models without stochastic differential equations: A tensor network approach

Diffusion models (DMs) are a class of generative machine learning methods that sample a target distribution by transforming samples of a trivial (often Gaussian) distribution using a learned stochastic differential equation. In standard DMs, this is done by learning a “score function” that reverses the effect of adding diffusive noise to the distribution of interest. Here we consider the generalisation of DMs to lattice systems with discrete degrees of freedom, and where noise is added via Markov chain jump dynamics. We show how to use tensor networks (TNs) to efficiently define and sample such “discrete diffusion models” (DDMs) without explicitly having to solve a stochastic differential equation. We show the following: (i) by parametrising the data and evolution operators as TNs, the denoising dynamics can be represented exactly; (ii) the auto-regressive nature of TNs allows to generate samples efficiently and without bias; (iii) for sampling Boltzmann-like distributions, TNs allow to construct an efficient learning scheme that integrates well with Monte Carlo. We illustrate this approach to study the equilibrium of two models with non-trivial thermodynamics, the d = 1 constrained Fredkin chain and the d = 2 Ising model. Published by the American Physical Society 2025

Causer, Luke (ORCID:0000000194243473)↗
DOE OSTI2018

Structures and diffusion of two-dimensional dusty plasmas on one-dimensional periodic substrates

Using numerical simulations, we examine the structure and diffusion of a two-dimensional dusty plasma (2DDP) in the presence of a one-dimensional periodic substrate (1DPS) as a function of increasing substrate strength. Both the pair correlation function perpendicular to the substrate modulation and the mean-squared displacement (MSD) of dust particles are calculated. It is found that both the structure and dynamics of 2DDP exhibit strong anisotropic effects, due to the applied 1DPS. As the substrate strength increases from zero, the structure order of dusty plasma along each potential well of 1DPS increases first probably due to the competition between the interparticle interactions and the particle-substrate interactions, and then decreases gradually, which may be due to the reduced dimensionality and the enhanced fluctuations. The obtained MSD along potential wells of 1DPS clearly shows three processes of diffusion in our studied 2DDP. Finally, between the initial ballistic and finally diffusive motion, there is the intermediate subdiffusion discovered here, which may result from the substrate-induced distortion of the caging dynamics.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2018

Diffusion of self-propelled particles in complex media

The diffusion of active microscopic organisms in complex environments plays an important role in a wide range of biological phenomena from cell colony growth to single organism transport. Here, we investigate theoretically and computationally the diffusion of a self-propelled particle (the organism) embedded in a complex medium composed of a collection of nonmotile solid particles that mimic soil or other cells. Under such conditions we find that the rotational relaxation time of the swimming direction depends on the swimming velocity and is drastically reduced compared to a pure Newtonian fluid. This leads to a dramatic increase (of several orders of magnitude) in the effective rotational diffusion coefficient of the self-propelled particles, which can lead to “self-trapping” of the active particles in such complex media. An analytical model is put forward that quantitatively captures the computational results. Our work sheds light on the role that the environment plays in the behavior of active systems and can be generalized in a straightforward fashion to understand other synthetic and biological active systems in heterogenous environments.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Kinetic Theory of Spin Diffusion and Superdiffusion in X X Z Spin Chains

We address the nature of spin transport in the integrable XXZ spin chain, focusing on the isotropic Heisenberg limit. We calculate the diffusion constant using a kinetic picture based on generalized hydrodynamics combined with Gaussian fluctuations: we find that it diverges, and show that a self-consistent treatment of this divergence gives superdiffusion, with an effective time-dependent diffusion constant that scales as $D(t) \sim t^{1/3}$. This exponent had previously been observed in large-scale numerical simulations, but had not been theoretically explained. We briefly discuss XXZ models with easy-axis anisotropy $\Delta > 1$. Our method gives closed-form expressions for the diffusion constant $D$ in the infinite-temperature limit for all $\Delta > 1$. We find that $D$ saturates at large anisotropy, and diverges as the Heisenberg limit is approached, as $D \sim (\Delta - 1)^{-1/2}$.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Studies of charm quark diffusion inside jets using PbPb and pp collisions at $\sqrt{s_\mathrm{NN}} =$ 5.02 TeV

The first study of charm quark diffusion with respect to the jet axis in heavy ion collisions is presented. The measurement is performed using jets with $p^{jet}_T > 60 GeV / c$ and $D^0$ mesons with $p^D_T > 4 GeV / c$ in lead-lead (Pb-Pb) and proton-proton ($pp$) collisions at a nucleon-nucleon center-of-mass energy of $\sqrt{s_{NN}} = 5.02 TeV$, recorded by the CMS detector at the LHC. The radial distribution of $D^0$ mesons with respect to the jet axis is sensitive to the production mechanisms of the meson, as well as to the energy loss and diffusion processes undergone by its parent parton inside the strongly interacting medium produced in Pb-Pb collisions. When compared to Monte Carlo event generators, the radial distribution in $pp$ collisions is found to be well described by pythia, while the slope of the distribution predicted by sherpa is steeper than that of the data. In Pb-Pb collisions, compared to the $pp$ results, the $D^0$ meson distribution for $4 < p^D_T < 20 GeV / c$ hints at a larger distance on average with respect to the jet axis, reflecting a diffusion of charm quarks in the medium created in heavy ion collisions. At higher $p^D_T$, the Pb-Pb and $pp$ radial distributions are found to be similar.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

Role of short- and long-range ordering on diffusion in Ni-Al alloys

The varying degrees of short- and long-range order exhibited by multicomponent solids complicates a first-principles calculation of nondilute diffusion coefficients. Temporal and spatial variations in the local degree of order affect the migration barriers of individual hops and can result in strong correlations between successive hops that ultimately affect macroscopic transport coefficients. Here we report on a first-principles study of diffusion in Ni-rich Ni-Al alloys. We used cluster expansion Hamiltonians to describe the energies of the end states and migration barriers of each hop. Kinetic Monte Carlo simulations were performed to calculate macroscopic transport coefficients. Variations in the degree of ordering are shown to play a significant role in affecting diffusion coefficients. While Al has a higher mobility than Ni in the disordered Ni-rich fcc solid solution, it becomes significantly less mobile in the ordered L1 2 γ' phase due to strong thermodynamic tendencies that keep Al trapped to disconnected sublattice sites.

36 MATERIALS SCIENCE↗
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 OSTI2018

Internal Resistive Barriers Related to Zinc Diffusion During the Growth of Inverted Metamorphic Multijunction Solar Cells

Majority carrier barriers at heterointerfaces are a common source of non-linear resistance that hinders concentrator solar cell performance. The source of a particular barrier is often unclear in a multijunction device with numerous heterointerfaces. In this work, we demonstrate Zn-dopant diffusion during inverted metamorphic multijunction (IMM) device growth to be one key cause of internal barrier formation. Using an inverted GaAs/GaAs tandem solar cell with a high temperature annealing layer grown in between each subcell, we simulate the annealing conditions of a multijunction growth in a simplified structure. Through analysis of the device by secondary ion mass spectrometry (SIMS) and electrochemical capacitance-voltage profiling, we show that annealing causes Zn to diffuse out of the top cell Ga 0.5 In 0.5 P back surface field (BSF) and accumulate in the GaAs base. Through equilibrium band modeling, we show that the resultant doping profile forms an energetic barrier to hole flow in the valence band, which correlates with fill factor losses in the current-voltage curves measured under concentration. When we, instead, employ a C-doped Al 0.2 Ga 0.8 As BSF layer in the top cell, we do not observe evidence of a heterojunction barrier. We attribute this difference to the reduced diffusivity of carbon, confirmed by SIMS, as well as more favorable valence band offsets between GaAs and Al 0.2 Ga 0.8 As. Finally, we compare 5-junction IMM cells with Al 0.2 Ga 0.8 As:C and Ga 0.5 In 0.5 P:Zn BSF layers in the GaAs third junction, respectively, and show a significantly improved concentrator device performance when Al 0.2 Ga 0.8 As:C is employed. Lastly, we demonstrate the importance of designing annealing tolerance into multijunction structures that are subjected to extended annealing during growth.

14 SOLAR ENERGY↗
DOE OSTI2021

Dopant Diffusion Control for Improved Tandem Cells Grown by D-HVPE

GaInP top cell current-density presently limits the performance of HVPE-grown two-junction devices, in large part due to unwanted dopant diffusion. Here, we institute mitigation strategies to lower the diffusion of dopants from both the front contact and back surface field. Successful application of these strategies resulted in a short-circuit current density of 12.1 mA/cm 2 in a GaInP/GaAs cell, an improvement of 0.9 mA/cm2 over our previous best cell. The reduced Se diffusion results in a thinner unpassivated emitter, which can lead to higher series resistance. Despite the increased resistance we obtained an efficiency increase from 23.7% to 24.8%.

42 ENGINEERING↗
DOE OSTI2025

Importance Sampling Model-Based Diffusion for Trajectory Optimization

Trajectory optimization for robotic systems remains a challenging problem. This is especially true for robotic systems featuring nonlinear dynamics and many degrees of freedom. Data-based or model-free diffusion has recently been popularized in the fields of artificial intelligence and trajectory optimization. Model-Based Diffusion provides a data-free method of trajectory optimization, trained at runtime on a system dynamics model, suitable for high-dimensional models. This paper examines how importance sampling can enhance the performance of Model-Based Diffusion for trajectory optimization. Here, we quantify the benefits of importance sampling across three long horizon planning tasks. These results show as much as a 13x improvement in sample efficiency depending on environment and optimization parameters.

Golembeski, Seth [Georgia Institute of Technology,↗
DOE OSTI2022

Coupled diffusion-deformation-damage model for polymers used in hydrogen infrastructure

The soft materials used in the infrastructure of hydrogen storage and distribution systems are vulnerable because exposure to high-pressure hydrogen can lead to mechanical damage and property degradation. Some materials, such as polymers, can undergo mechanical failure due to a phenomenon wherein hydrogen gas diffuses through the polymer chains and occupies preexisting cavities or voids inside the polymer material. If the hydrogen gas pressure is reduced more quickly than hydrogen can diffuse back out of the polymer which is necessary for some applications, the trapped hydrogen gas instead escapes by rupturing the material, causing surface blistering or permanent damage. In this study, a continuum mechanics-based fully coupled diffusion-deformation model with damage is developed to predict the stress distribution and damage propagation while the polymer undergoes rapid decompression failure. The hyperelastic material model, along with the maximum principal strain failure theory, was chosen for this study as it represents the nonlinear material response with brittle failure observed in uniaxial tensile tests perfectly. EPDM polymer was chosen for this study because of its commercial availability and common use in hydrogen storage and distribution system. It has superior mechanical properties, high and low temperature resistance, and certain compounds work well in hydrogen gas. This work is useful for design engineers to alter the parameters while manufacturing polymer composites to increase their performance in a high-pressure hydrogen environment.

Kulkarni, Shank S.↗
DOE OSTI2024

Persistent enhancement of exciton diffusivity in CsPbBr 3 nanocrystal solids

In semiconductors, exciton or charge carrier diffusivity is typically described as an inherent material property. Here, we show that the transport of excitons among CsPbBr 3 perovskite nanocrystals (NCs) depends markedly on how recently those NCs were occupied by a previous exciton. Using transient photoluminescence microscopy, we observe a striking dependence of the apparent exciton diffusivity on excitation laser power that does not arise from nonlinear exciton-exciton interactions or thermal heating. We interpret our observations with a model in which excitons cause NCs to transition to a long-lived metastable configuration that markedly increases exciton transport. The exciton diffusivity observed here (>0.15 square centimeters per second) is considerably higher than that observed in other NC systems, revealing unusually strong excitonic coupling between NCs. The finding of a persistent enhancement in excitonic coupling may help explain other photophysical behaviors observed in CsPbBr 3 NCs, such as superfluorescence, and inform the design of optoelectronic devices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Mapping structures and dynamics with frequency-correlated diffusion exchange

Understanding molecular motion in diffusion-driven complex environments is critical for designing sustainable materials and improving chemical processes. Here, we introduce a multidimensional nuclear magnetic resonance (NMR) method that captures how molecular populations exchange across different dynamic regimes. By extending the modulated gradient spin-echo technique to include frequency-frequency correlations, our approach reveals diffusion pathways that are otherwise obscured in heterogeneous systems. Implemented on a unilateral NMR magnet, the method eliminates gradient pulsing constraints and accesses dynamics in the kilohertz regime. We apply this technique to swelling and acid-catalyzed deconstruction of cross-linked and linear polymers to observe how structural heterogeneity evolves over time. By linking molecular motion to topology and chemical state, we extract physical metrics such as fractal surface dimensionality and reaction wavefront velocity, properties inaccessible with standard diffusion measurements. This work expands the capabilities of NMR for probing soft matter, with implications for polymer recycling and materials design.

Fricke, Sophia N. [University of California, Berke↗