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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

3D printed carbon fiber reinforced carbon as an energy efficient alternative to graphite for EFAS tooling

As Electric Field Assisted Sintering (EFAS) gains more industrial acceptance and use, it becomes more important to develop more efficient means to implement this technology. To this aim, 3D printed continuous carbon fiber reinforced carbon (CCC) was manufactured and fabricated into tooling for EFAS systems as an alternative to traditional graphite tooling. The impact of fiber orientation on the thermal and electrical properties of the CCC was characterized. Sample material was sintered in Tokai G535 graphite tooling, under common processing conditions and compared with CCC tooling. There was nearly 50 % energy savings compared to graphite while maintaining equivalent sample density and microstructure plus keeping ram temperatures 39 % cooler. This is due to spatial control of generated heat and thermal diffusivity within the molds, by means of fiber orientation anisotropy. Finite element modeling of the tooling design supported the experimental results as well as displays the effect of optimization of this 3D printed CCC material.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Diffusion Model-Guided Inverse Design of Bimetallic Catalysts for Ammonia Decomposition

In the past decade, artificial intelligence and deep learning have played increasingly prominent roles in materials design and discovery. Among these, generative AI models, known for their ability to create unique and complex structures, have emerged as state-of-the-art tools for materials screening due to their high efficiency and low computational cost. In catalysis, one of the major challenges is identifying promising material candidates within an immense chemical space. This challenge can be addressed using generative approaches, such as diffusion-based inverse design models. In this study, we present a machine learning-guided workflow that employed a diffusion model for the inverse design of bimetallic alloy catalysts for low-carbon ammonia decomposition, a key reaction for ammonia emission control and sustainable hydrogen production. Catalyst candidates were evaluated using nitrogen adsorption energy as the key descriptor, inspired by multiscale modeling. The proposed workflow identified low-cost, environmentally friendly catalysts with excellent catalytic performance, which have been validated theoretically and experimentally. Our framework decoupled the generative and property-prediction components, enhancing both flexibility and accuracy in the catalytic material design process.

Adsorption↗
DOE OSTI2025

Structural differences between human and mouse neurons and their implementation in generative AIs

Mouse and human brains have different functions that depend on their neuronal networks. We analyzed nanometer-scale three-dimensional structures of brain tissues of the mouse medial prefrontal cortex and compared them with structures of the human anterior cingulate cortex. The obtained results indicated that mouse neuronal somata are smaller and neurites are thinner than those of human neurons. We implemented these characteristics of mouse neurons in convolutional layers of a generative adversarial network (GAN) and a denoising diffusion implicit model (DDIM), which were then subjected to image generation tasks using photo datasets of cat faces, cheese, human faces, birds, and automobiles. The mouse-mimetic GAN outperformed a standard GAN in the image generation task using the cat faces and cheese photo datasets, but underperformed for human faces and birds. The mouse-mimetic DDIM gave similar results, suggesting that the nature of the datasets affected the results. Analyses of the five datasets indicated differences in their image entropy, which should influence the number of parameters required for image generation. The preferences of the mouse-mimetic AIs coincided with the impressions commonly associated with mice. The relationship between the neuronal network and brain function should be investigated by implementing other biological findings in artificial neural networks.

generative AI↗
DOE OSTI2026

Generative AI in Supply Chain Management: Applications, Challenges, and Future Directions

Supply chain management (SCM) is undergoing rapid transformation due to increasing global complexity, demand volatility, and operational disruptions. Generative Artificial Intelligence (GenAI) has emerged as a powerful paradigm capable of synthesizing data, simulating operational scenarios, and enabling adaptive decision-making across supply chain networks. This paper presents a survey of GenAI’s role in SCM, focusing on its applications in predictive analytics, autonomous logistics, and fraud detection. Unlike traditional AI systems that rely primarily on predictive analytics, GenAI models, including large language models, generative adversarial networks, and diffusion-based architectures, enable the creation of synthetic supply chain scenarios and autonomous optimization strategies. This survey provides (1) a taxonomy of GenAI techniques for supply chain applications, (2) a comparative analysis of generative AI approaches with traditional machine learning, reinforcement learning, and blockchain-based methods, and (3) a discussion of key challenges such as data privacy, interpretability, and integration with legacy enterprise systems. Furthermore, we outline open research problems and propose directions for future research toward autonomous, resilient, and sustainable AI-driven supply chains.

15 - GEOTHERMAL ENERGY↗
DOE OSTI2025

CaloChallenge 2022: a community challenge for fast calorimeter simulation

Here, we present the results of the ‘Fast Calorimeter Simulation Challenge 2022’—the CaloChallenge. We study state-of-the-art generative models on four calorimeter shower datasets of increasing dimensionality, ranging from a few hundred voxels to a few tens of thousand voxels. The 31 individual submissions span a wide range of current popular generative architectures, including variational autoencoders (VAEs), generative adversarial networks (GANs), normalizing flows, diffusion models, and models based on conditional flow matching. We compare all submissions in terms of quality of generated calorimeter showers, as well as shower generation time and model size. To assess the quality we use a broad range of different metrics including differences in one-dimensional histograms of observables, KPD/FPD scores, AUCs of binary classifiers, and the log-posterior of a multiclass classifier. The results of the CaloChallenge provide the most complete and comprehensive survey of cutting-edge approaches to calorimeter fast simulation to date. In addition, our work provides a uniquely detailed perspective on the important problem of how to evaluate generative models. As such, the results presented here should be applicable for other domains that use generative AI and require fast and faithful generation of samples in a large phase space.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Random Walks With Tweedie: A Unified View of Score-Based Diffusion Models [In the Spotlight]

We present a concise derivation for several influential score-based diffusion models that relies on only a few textbook results. Diffusion models have recently emerged as powerful tools for generating realistic, synthetic signals—particularly natural images—and often play a role in state-of-the-art algorithms for inverse problems in image processing. While these algorithms are often surprisingly simple, the theory behind them is not, and multiple complex theoretical justifications exist in the literature. Here, in this study, we provide a simple and largely self-contained theoretical justification for score-based diffusion models that is targeted towards the signal processing community. This approach leads to generic algorithmic templates for training and generating samples with diffusion models. We show that several influential diffusion models correspond to particular choices within these templates and demonstrate that alternative, more straightforward algorithmic choices can provide comparable results. This approach has the added benefit of enabling conditional sampling without any likelihood approximation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Plasma instabilities dominate radioactive transients magnetic fields: the self-confinement of leptons in Type Ia and core-collapse supernovae, and kilonovae

The light curves of radioactive transients, such as supernovae and kilonovae, are powered by the decay of radioisotopes, which release high-energy leptons through $\beta ^+$ and $\beta ^-$ decays. These leptons deposit energy into the expanding ejecta. As the ejecta density decreases during expansion, the plasma becomes collisionless, with particle motion governed by electromagnetic forces. In such environments, strong or turbulent magnetic fields are thought to confine particles, though the origin of these fields and the confinement mechanism have remained unclear. Using fully kinetic particle-in-cell (PIC) simulations, we demonstrate that plasma instabilities can naturally confine high-energy leptons. These leptons generate magnetic fields through plasma streaming instabilities, even in the absence of pre-existing fields. The self-generated magnetic fields slow lepton diffusion, enabling confinement, and transferring energy to thermal electrons and ions. Our results naturally explain the positron trapping inferred from late-time observations of thermonuclear and core-collapse supernovae. Furthermore, they suggest potential implications for electron dynamics in the ejecta of kilonovae. We also estimate synchrotron radio luminosities from positrons for Type Ia supernovae and find that such emission could only be detectable with next-generation radio observatories from a Galactic or local-group supernova in an environment without any circumstellar material.

instabilities↗
DOE OSTI2025

Plastic from CO 2 , Water, and Electricity: Tandem Electrochemical CO 2 Reduction and Thermochemical Ethylene-CO Copolymerization

Converting CO 2 into industrially useful products is an appealing strategy for utilization of an abundant chemical resource. Electrochemical CO 2 reduction (eCO 2 R) offers a pathway to convert CO 2 into CO and ethylene, using renewable electricity. Here, these products can be efficiently copolymerized by organometallic catalysts to generate polyketones. However, the conditions for these reactions are very different, presenting the challenge of coupling microenvironments typically encountered for the transformation of CO 2 into highly complex but desirable multicarbon products. Herein, we present a system to produce polyketone plastics entirely derived from CO 2 and water, where both the CO and C 2 H 4 intermediates are produced by eCO 2 R. In this system, a combination of Cu and Ag gas diffusion electrodes is used to generate a gas mixture with nearly equal concentrations of CO and C 2 H 4 , and a recirculatory CO 2 reduction loop is used to reach concentrations of above 11% each, leading to a current-to-polymer efficiency of up to 51% and CO 2 utilization of 14%.

carbon storage↗
DOE OSTI2025

Point cloud-based diffusion models for the Electron-Ion Collider

At high-energy collider experiments, generative models can be used for a wide range of tasks, including fast detector simulations, unfolding, searches of physics beyond the Standard Model, and inference tasks. In particular, it has been demonstrated that score-based diffusion models can generate high-fidelity and accurate samples of jets or collider events. This work expands on previous generative models in three distinct ways. First, our model is trained to generate entire collider events, including all particle species with complete kinematic information. We quantify how well the model learns event-wide constraints such as the conservation of momentum and discrete quantum numbers. We focus on the events at the future Electron-Ion Collider, but we expect that our results can be extended to proton-proton and heavy-ion collisions. Second, previous generative models often relied on image-based techniques. The sparsity of the data can negatively affect the fidelity and sampling time of the model. We address these issues using point clouds and a novel architecture combining edge creation with transformer modules called Point Edge Transformers. Third, we adapt the foundation model OmniLearn, to generate full collider events. This approach may indicate a transition toward adapting and fine-tuning foundation models for downstream tasks instead of training new models from scratch.

Araz, Jack Y. [Stony Brook Univ., NY (United State↗
DOE OSTI2025

Superionic-like diffusion in yttrium dihydride

For the next-generation high temperature microreactors, yttrium dihydride (YH 2 ) is an attractive solid state neutron moderator. Despite a number of recent investigations, the mechanism of hydrogen transport remains poorly understood. Experimental evaluations of diffusivity are inconclusive with large variations in diffusivities and activation energies. In this work, we perform ab initio molecular dynamics (AIMD) simulations on YH 2 for temperatures spanning 300 K to 1200 K. Our main finding is that YH 2 shows a superionic-like behavior with hydrogen atoms hopping from one native site to another above a characteristic temperature of 800 K. This correlated motion results in quasi-one-dimensional string-like displacements that enable the hydrogen atoms to diffuse rapidly. We confirm that the octahedral sites are mostly unoccupied, although channeling through them is the most favored pathway between lattice hops above 800 K. At the highest temperature of 1200 K, the string relaxation time is merely of the order of a few picoseconds, which indicates a liquid-like diffusive behavior. Based on the formation of spontaneous thermal vacancies, an order-disorder crossover temperature T α ~ 800 K is established for YH 2 with an activation energy of 0.83 eV for hydrogen diffusion in the superionic-like state.

Superionic-like Diffusion↗
DOE OSTI2024

Stabilized Oxygen Vacancy Chemistry toward High-Performance Layered Oxide Cathodes for Sodium-Ion Batteries

Anionic redox has emerged as a transformative paradigm for high-energy layered transition-metal (TM) oxide cathodes, but it is usually accompanied by the formation of anionic redox-mediated oxygen vacancies (OVs) due to irreversible oxygen release. Additionally, external factor-induced OVs (defined as intrinsic OVs) also play a pivotal role in the physicochemical properties of layered TM oxides. However, an in-depth understanding of the interplay between intrinsic and anionic redox-mediated OVs and the corresponding regulation mechanism of the dynamic evolution of OVs is still missing. Herein, we disclose the strong interrelationship between these OVs and demonstrate that the presence of intrinsic OVs in the TMO2 layers could induce weak integrity of the TM-O frameworks and unlock additional diffusion paths to trigger the generation and migration of anionic redox-mediated OVs. Accordingly, an OV stabilization strategy is proposed by deliberately introducing high-valence Nb5+, which could serve as an important building block in anchoring the oxygen sublattice and preventing the formation of a percolating OV migration network, thereby suppressing the formation/diffusion of anionic redox-mediated OVs. Consequently, superb structural integrity and improved electrochemical performance with reversible anionic redox chemistry are achieved. This work advances our understanding of the role of OVs for developing high-performance energy storage systems utilizing anionic redox.

anionic redox↗
DOE OSTI2024

DiffESM: Conditional Emulation of Temperature and Precipitation in Earth System Models With 3D Diffusion Models

Earth system models (ESMs) are essential for understanding the interaction between human activities and the Earth's climate. However, the computational demands of ESMs often limit the number of simulations that can be run, hindering the robust analysis of risks associated with extreme weather events. While low-cost climate emulators have emerged as an alternative to emulate ESMs and enable rapid analysis of future climate, many of these emulators only provide output on at most a monthly frequency. This temporal resolution is insufficient for analyzing events that require daily characterization, such as heat waves or heavy precipitation. We propose using diffusion models, a class of generative deep learning models, to effectively downscale ESM output from a monthly to a daily frequency. Trained on a handful of ESM realizations, reflecting a wide range of radiative forcings, our DiffESM model takes monthly mean precipitation or temperature as input, and is capable of producing daily values with statistical characteristics close to ESM output. Combined with a low-cost emulator providing monthly means, this approach requires only a small fraction of the computational resources needed to run a large ensemble. We evaluate model behavior using a number of extreme metrics, showing that DiffESM closely matches the spatio-temporal behavior of the ESM output it emulates in terms of the frequency and spatial characteristics of phenomena such as heat waves, dry spells, or rainfall intensity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Non-classical ion-indiffusion processes in quasi-one-dimensional ionic conductor KTiOPO4

Developments in coercive field engineering and waveguide writing via ion-exchange in the ferroelectric ionic-conductor KTiOPO4 (KTP) have led to breakthroughs within the field of nonlinear optics and quantum photonics. However, the microscopic diffusion dynamics of ion-exchange in KTP are poorly understood and show signs of non-classical diffusion dynamics. The precise control of ion-exchange gradients is critical for developing KTP into a mature, integrated nonlinear optical material but requires a level of control that, in turn, demands a deeper understanding of the ion-exchange process. We employ direct ToF-SIMS mapping to untangle the complex diffusion dynamics of the commonly used ion-exchange dopant ions: Rb, Ba, and K. We map periodic diffusion gradients of two exchanges, one dominated by Rb-ions and the other by Ba-ions. For the first time, we show the interdependent nature of the Ba- and Rb ion in the ion-exchange diffusion gradient. The low incorporation of the Ba ion is mapped and quantified, and dopant–crystal interactions are identified. This work highlights the complex diffusion dynamics of dopants in the KTP structure and lays the foundation for deterministic control of diffusion gradients for the next generation of integrated nonlinear and quantum photonic devices.

Barrett, Laura [ORNL]↗
DOE OSTI2026

Statistical inference of anomalous thermal transport with uncertainty quantification for interpretive 2D SOL models

The critical task of inferring anomalous cross-field transport coefficients is addressed in simulations of boundary plasmas with fluid models. A workflow for parameter inference in the UEDGE fluid code is developed using Bayesian optimization with parallelized sampling and integrated uncertainty quantification. In this workflow, transport coefficients are inferred by maximizing their posterior probability distribution, which is generally multidimensional and non-Gaussian. Uncertainty quantification is integrated throughout the optimization within the Bayesian framework that combines diagnostic uncertainties and model limitations. As a concrete example, we infer the anomalous electron thermal diffusivity $\chi_\perp$ from an interpretive 2D model describing electron heat transport in the conduction-limited region with radiative power loss. The workflow is first benchmarked against synthetic data and then tested on H-, L-, and I-mode discharges to match their midplane temperature and divertor heat flux profiles. We demonstrate that the workflow efficiently infers diffusivity and its associated uncertainty, generating 2D profiles that match 1D measurements. Future efforts will focus on incorporating more complicated fluid models and analyzing transport coefficients inferred from a large database of experimental results.

Bayesian optimization↗
DOE OSTI2025

A comparison of probabilistic generative frameworks for molecular simulations

Generative artificial intelligence is now a widely used tool in molecular science. Despite the popularity of probabilistic generative models, numerical experiments benchmarking their performance on molecular data are lacking. Here, in this work, we introduce and explain several classes of generative models, broadly sorted into two categories: flow-based models and diffusion models. We select three representative models: neural spline flows, conditional flow matching, and denoising diffusion probabilistic models, and examine their accuracy, computational cost, and generation speed across datasets with tunable dimensionality, complexity, and modal asymmetry. Our findings are varied, with no one framework being the best for all purposes. In a nutshell, (i) neural spline flows do best at capturing mode asymmetry present in low-dimensional data, (ii) conditional flow matching outperforms other models for high-dimensional data with low complexity, and (iii) denoising diffusion probabilistic models appear the best for low-dimensional data with high complexity. Our datasets include a Gaussian mixture model and the dihedral torsion angle distribution of the Aib9 peptide, generated via a molecular dynamics simulation. We hope our taxonomy of probabilistic generative frameworks and numerical results may guide model selection for a wide range of molecular tasks.

Artificial intelligence↗
DOE OSTI2026

Extending SLUSCHI for Automated Diffusion Calculations

We present an extension of the SLUSCHI package (Solid and Liquid in Ultra Small Coexistence with Hovering Interfaces) to enable automated diffusion calculations from first-principles molecular dynamics. While the original SLUSCHI workflow was designed for melting temperature estimation via solid-liquid coexistence, we adapt its input and output handling to isolate the volume search stage and generate one production trajectory suitable for diffusion analysis. Post-processing tools parse VASP outputs, compute mean-square displacements (MSD), and extract tracer diffusivities using the Einstein relation with robust error estimates through block averaging. Diagnostic plots, including MSD curves, running slopes, and velocity autocorrelations, are produced automatically to help identify diffusive regimes. The method has been validated through representative case studies: self-diffusion in Al-Cu liquid alloys, sublattice melting in Li7La3Zr2O12 and Er2O3, interstitial oxygen transport in bcc and fcc Fe, and oxygen diffusivity in Fe-O liquids with variable Si and Al contents. Viscosity and diffusivity are linked through the Stokes-Einstein relation, with composition dependence assessed via simple linear mixing. This capability broadens SLUSCHI from melting-point predictions to transport property evaluation, enabling high-throughput, fully first-principles datasets of diffusion coefficients and viscosities across metals and oxides.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Collision operator for electron runaway in cold weakly ionised plasmas

In cold weakly ionised plasmas, the Dreicer generation mechanism can be non-diffusive as demonstrated in a recent study. By expanding the previous letter, we present the detailed description of a proper collision operator to precisely account for the non-diffusive electron kinetics. The operator appropriately combines the Fokker–Planck operator and Boltzmann operator where free-bound collision cross-sections are valid in the low-energy region. The proposed operator is envisaged to predict runaway electron generation in cold weakly ionised plasmas, particularly to design a runaway-free reactor tokamak start-up.

fusion plasma↗