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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Cyber Halo Innovation Research Program (CHIRP) Handbook: CHIRP Program Document 2026

The Cyber Halo Innovation Research Program (CHIRP) handbook outlines a comprehensive framework designed to advance space cybersecurity education, workforce development, recruitment efforts for United States Space Force (USSF) Space Systems Command (SSC) and the Department of the Airforce, and foster students’ professional growth. It provides an overview of CHIRP's objectives and strategic focus, establishing the foundation for participant engagement through a network of collaborations with academic institutions, contracted industry partners, training and certification organizations, federal agencies, and community organizations. It states a clear participation strategy for SSC and Pacific Northwest National Laboratory (PNNL) for program execution and successful support for student transition to a career in space cybersecurity.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2026

Library of Advanced Materials for Engineering (LAMÉ) 5.30

Accurate and efficient constitutive modeling remains a cornerstone issue for solid mechanics analysis. Over the years, the LAMÉ advanced material model library has grown to address this challenge by implementing models capable of describing material systems spanning soft polymers to stiff ceramics including both isotropic and anisotropic responses. Inelastic behaviors including (visco)plasticity, damage, and fracture have all incorporated for use in various analyses. This multitude of options and flexibility, however, comes at the cost of many capabilities, features, and responses and the ensuing complexity in the resulting implementation. Therefore, to enhance confidence and enable the utilization of the LAMÉ library in application, this effort seeks to document and verify the various models in the LAMÉ library. Specifically, the broader strategy, organization, and interface of the library itself is first presented. The physical theory, numerical implementation, and user guide for a large set of models is then discussed. Importantly, a number of verification tests are performed with each model to not only have confidence in the model itself but also highlight some important response characteristics and features that may be of interest to end-users. Finally, in looking ahead to the future, approaches to add material models to this library and further expand the capabilities are presented.

36 MATERIALS SCIENCE↗
DOE OSTI2026

MatLib-1.2.2: Nuclear Material Properties Library

The U.S. Nuclear Regulatory Commission (NRC) uses the computer code Fuel Analysis under Steady-state and Transients (FAST) to model steady-state and transient fuel behavior to support regulatory decisions. FAST relies on a material properties library (MatLib) that contains the thermal and mechanical properties of the nuclear materials and coolants of interest to support the U.S. commercial nuclear industry. MatLib contains properties for a variety of nuclear fuels, cladding and other structural materials, gases, and coolants. In this document, material property correlations for the materials contained within MatLib are presented and discussed. When available, comparisons are made between the material property correlations and available data. Additionally, uncertainties are quantified on the material properties, which is then used by the NRC to support uncertainty quantification for best-estimate plus uncertainty safety evaluation reviews. This document describes MatLib-1.2.2, updated from MatLib-1.2.1 to include additional properties for metallic fuel. It is one of a series of documents on FAST; the other documents detail the models used by FAST as well as its integral assessment to experiments and commercial data.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Machine Learning for Multipactor Susceptibility Prediction in Planar RF Gaps

Multipactor discharge is a nonlinear electron avalanche that limits the performance of high-power radio-frequency (RF) and vacuum electronic devices. Predicting multipactor susceptibility traditionally relies on Monte Carlo or particle-in-cell (PIC) simulations, which become computationally expensive for large parametric studies. In this work, we present a supervised machine-learning (ML) framework for prediction of multipactor susceptibility in a two-surface planar geometry. The models are trained using high-fidelity PIC simulation generated susceptibility data and learn the relationship between operational parameters, geometry, and material-dependent secondary electron emission properties. The proposed approach enables rapid reconstruction of susceptibility charts while preserving the physical structure of multipactor growth regions.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Two-Stage Optimization Framework for Detecting and Correcting Parameter Cyber-Attacks in Power System State Estimation

One major tool of Energy Management Systems for monitoring the status of the power grid is State Estimation. Since the results of state estimation are used within the energy management system, the security of the state estimation process is most important. The focus research in this area is on detecting False Data Injection attacks on measurements. While this is important, State Estimation also rely on database that are used to describe the relationship between measurements and systems' states. This paper presents a two-stage programming framework to detect and correct attacks in the parameters of the measurement model used by the state estimation process in the Energy Management System. In the first stage, an estimate of the line parameters ratios are obtained. In the second stage, the estimated ratios from stage I are used in a Bi-Level model for obtaining a final estimate of the measurements' model parameters. Hence, the presented framework does not only unify the detection and correction in a single optimization run, but also provide a monitoring scheme for the SE database that is typically considered static. In addition, in the two stages, linear programming framework is preserved. For validation, the IEEE 118 bus system is used for implementation. The results of this paper illustrate the effectiveness of the proposed model for detecting attacks in the database used in the state estimation process.

state estimation, two-stage optimization, cyber-ph↗
DOE OSTI2025

Modeling suggests SARS-CoV-2 rebound after nirmatrelvir-ritonavir treatment is driven by target cell preservation coupled with incomplete viral clearance

In a subset of SARS-CoV-2-infected individuals treated with the antiviral nirmatrelvir-ritonavir, the virus rebounds following treatment. The mechanisms driving this rebound are not well understood. We used a mathematical model to describe the longitudinal viral load dynamics of 51 individuals treated with nirmatrelvir-ritonavir, 20 of whom rebounded. Target cell preservation, either by a robust innate immune response or initiation of N-R near the time of symptom onset, coupled with incomplete viral clearance, appears to be the main factor leading to viral rebound. Moreover, the occurrence of viral rebound is likely influenced by the time of treatment initiation relative to the progression of the infection, with earlier treatments leading to a higher chance of rebound. A comparison with an untreated cohort suggests that early treatments with nirmatrelvir-ritonavir may be associated with a delay in the onset of an adaptive immune response. Nevertheless, our model demonstrates that extending the course of nirmatrelvir-ritonavir treatment to a 10-day regimen may greatly diminish the chance of rebound in people with mild-to-moderate COVID-19 and who are at high risk of progression to severe disease. Altogether, our results suggest that in some individuals, a standard 5-day course of nirmatrelvir-ritonavir starting around the time of symptom onset may not completely eliminate the virus. Thus, after treatment ends, the virus can rebound if an effective adaptive immune response has not fully developed. These findings on the role of target cell preservation and incomplete viral clearance also offer a possible explanation for viral rebounds following other antiviral treatments for SARS-CoV-2.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Transitory Topochemical Tailoring of a van der Waals Superconductor

Topochemical intercalation is widely used to access metastable phases with novel electronic properties, but the reverse reaction (deintercalation) typically restores the original state, limiting practical use. Here, in this study, we present a topochemical approach that employs a sacrificial intercalant that thermally decomposes to irreversibly lock in the new electronic state. Using 2-aminobutane as the sacrificial intercalant, we convert the van der Waals (vdW) material 1T-TiSe 2 into a superconductor and the vdW superconductor 2H-NbSe 2 into a nonsuperconducting metal, while preserving the ability to exfoliate the resulting crystals. We find that this transitory intercalation increases the electron density in both materials and partially suppresses the CDW in TiSe 2 . By tuning the thermolysis temperature, we can systematically vary the carrier density in TiSe 2 , enabling us to map its phase diagram. The superconductivity in TiSe 2 is retained in exfoliated flakes, although with a lower critical temperature. This transitory topochemical strategy enables access to new electronic states with precisely tuned carrier densities that are otherwise inaccessible through direct solid-state synthesis.

36 MATERIALS SCIENCE↗
DOE OSTI2022

SPANet: Generalized permutationless set assignment for particle physics using symmetry preserving attention

The creation of unstable heavy particles at the Large Hadron Collider is the most direct way to address some of the deepest open questions in physics. Collisions typically produce variable-size sets of observed particles which have inherent ambiguities complicating the assignment of observed particles to the decay products of the heavy particles. Current strategies for tackling these challenges in the physics community ignore the physical symmetries of the decay products and consider all possible assignment permutations and do not scale to complex configurations. Attention based deep learning methods for sequence modelling have achieved state-of-the-art performance in natural language processing, but they lack built-in mechanisms to deal with the unique symmetries found in physical set-assignment problems. We introduce a novel method for constructing symmetry-preserving attention networks which reflect the problem's natural invariances to efficiently find assignments without evaluating all permutations. This general approach is applicable to arbitrarily complex configurations and significantly outperforms current methods, improving reconstruction efficiency between 19% - 35% on typical benchmark problems while decreasing inference time by two to five orders of magnitude on the most complex events, making many important and previously intractable cases tractable. A full code repository containing a general library, the specific configuration used, and a complete dataset release, are available at https://github.com/Alexanders101/SPANet

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

A Hybrid Data-Driven and Model-Based Anomaly Detection Scheme for DER Operation

This paper proposes a hybrid data and model-based anomaly detection scheme to secure the operation of distributed energy resources (DERs) in distribution grids. Data-driven autoencoders are set up at the edge device level and they use local DER operational data as inputs. The abnormal statuses are detected by analyzing reconstruction errors. In parallel, modelbased state estimation (SE) is set up at the central level and it uses system-wide models and measurements as data inputs. The anomalies are identified by analyzing measurement residuals. The hybrid scheme preserves the benefits of both data-driven and model-based analyses and thus improves the robustness and the accuracy of anomaly detection. Numerical tests based on the model of a real distribution feeder in Southern California highlight the proposed scheme's effectiveness and benefits.

anomaly detection↗
DOE OSTI2021

Improved accuracy and transferability of molecular-orbital-based machine learning: Organics, transition-metal complexes, non-covalent interactions, and transition states

We report molecular-orbital-based machine learning (MOB-ML) provides a general framework for the prediction of accurate correlation energies at the cost of obtaining molecular orbitals. The application of Nesbet’s theorem makes it possible to recast a typical extrapolation task, training on correlation energies for small molecules and predicting correlation energies for large molecules, into an interpolation task based on the properties of orbital pairs. We demonstrate the importance of preserving physical constraints, including invariance conditions and size consistency, when generating the input for the machine learning model. Numerical improvements are demonstrated for different datasets covering total and relative energies for thermally accessible organic and transition-metal containing molecules, non-covalent interactions, and transition-state energies. MOB-ML requires training data from only 1% of the QM7b-T dataset (i.e., only 70 organic molecules with seven and fewer heavy atoms) to predict the total energy of the remaining 99% of this dataset with sub-kcal/mol accuracy. This MOB-ML model is significantly more accurate than other methods when transferred to a dataset comprising of 13 heavy atom molecules, exhibiting no loss of accuracy on a size intensive (i.e., per-electron) basis. It is shown that MOB-ML also works well for extrapolating to transition-state structures, predicting the barrier region for malonaldehyde intramolecular proton-transfer to within 0.35 kcal/mol when only trained on reactant/product-like structures. Finally, the use of the Gaussian process variance enables an active learning strategy for extending the MOB-ML model to new regions of chemical space with minimal effort. We demonstrate this active learning strategy by extending a QM7b-T model to describe non-covalent interactions in the protein backbone–backbone interaction dataset to an accuracy of 0.28 kcal/mol.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Effects of Synthesis Conditions on the Structure and Conductivity of Hydrogen-Substituted Graphdiyne

This study investigates how synthesis conditions influence the structure and conductivity of hydrogen-substituted graphdiyne (HsGDY). By varying the reaction temperature and solvent, we find that small changes in conditions markedly affect triple-bond retention and electronic continuity. Solid-state 13 C NMR and Raman spectroscopy reveal that elevated temperatures drive alkyne loss and partial graphitization, with N,N-dimethylformamide (DMF) promoting faster degradation than pyridine. The resulting decline in alkyne content directly correlates with reduced conductivity, indicating that preserving conjugation is essential for charge transport. These findings clarify how the synthetic environment governs the structural and electronic evolution of graphdiyne frameworks, providing insight into the controlled preparation of conjugated carbon networks.

alkyne retention↗
DOE OSTI2026

Data-driven particle dynamics: Structure-preserving coarse-graining for emergent behavior in non-equilibrium systems

Multiscale systems are ubiquitous in science and technology, but are notoriously challenging to simulate as short spatiotemporal scales must be appropriately linked to emergent bulk physics. When expensive high-dimensional dynamical systems are coarse-grained into low-dimensional models, the entropic loss of information leads to emergent physics which are dissipative, history-dependent, and stochastic. To machine learn coarse-grained dynamics from time-series observations of particle trajectories, we propose a framework using the metriplectic bracket formalism that preserves these properties by construction; most notably, the framework guarantees discrete notions of the first and second laws of thermodynamics, conservation of momentum, and a discrete fluctuation-dissipation balance crucial for capturing non-equilibrium statistics. We introduce the mathematical framework abstractly before specializing to a particle discretization. As labels are generally unavailable for entropic state variables, we introduce a novel self-supervised learning strategy to identify emergent structural variables. We validate the method on benchmark systems and demonstrate its utility on two challenging examples: (1) coarse-graining star polymers at challenging levels of coarse-graining while preserving non-equilibrium statistics, and (2) learning models from high-speed video of colloidal suspensions that capture coupling between local rearrangement events and emergent stochastic dynamics. We provide open-source implementations in both PyTorch and LAMMPS, enabling large-scale inference and extensibility to diverse particle-based systems.

Computational Engineering, Finance, and Science (c↗
DOE OSTI2025

An attention-based neural ordinary differential equation framework for modeling inelastic processes

To preserve strictly conservative behavior as well as model the variety of dissipative behavior displayed by solid materials, we propose a significant enhancement to the internal state variable-neural ordinary differential equation (ISV-NODE) framework. In this data-driven, physics-constrained modeling framework internal states are inferred rather than prescribed. The ISV-NODE consists of: (a) a stress model dependent on observable deformation and inferred internal state, and (b) a model of the evolution of the internal states. The enhancements to ISV-NODE proposed in this work are multifold: (a) a partially input convex neural network stress potential provides polyconvexity in terms of observed strain while leaving the inferred state unconstrained, and (b) an internal state flow model uses common latent features to inform novel attention-based gating and drives the flow of internal state only in dissipative regimes. We demonstrated that this architecture can accurately model dissipative and conservative behavior across an isotropic, isothermal elastic-viscoelastic-elastoplastic spectrum with three exemplars, while maintaining fundamental principles by design.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Realistic simulations of spin squeezing and cooperative coupling effects in large ensembles of interacting two-level systems

We describe an efficient numerical method for simulating the dynamics of interacting spin ensembles in the presence of dephasing and decay. The method builds on the discrete truncated Wigner approximation for isolated systems, which combines the mean-field dynamics of a spin ensemble with a Monte Carlo sampling of discrete initial spin values to account for quantum correlations. Here we show how this approach can be generalized for dissipative spin systems by replacing the deterministic mean-field evolution by a stochastic process, which describes the decay of coherences and populations while preserving the length of each spin. We demonstrate the application of this technique for simulating nonclassical spin-squeezing effects or the dynamics and steady states of cavity QED models with 10 5 interacting two-level systems. This opens up the possibility to perform accurate real-scale simulations of a diverse range of experiments in quantum optics or with solid-state spin ensembles under realistic laboratory conditions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Topological order in interacting semimetals

It has recently been demonstrated that it is possible to open a gap in a magnetic Weyl semimetal, while preserving the chiral anomaly along with the charge conservation and translational symmetries, which all protect the gapless nodes in a weakly interacting semimetal. The resulting state was shown to be a nontrivial generalization of a non-Abelian fractional quantum Hall liquid to three dimensions. Here we point out that a second fractional quantum Hall state exists in this case. This state has exactly the same electrical and thermal Hall responses as the first, but a distinct (fracton) topological order. Moreover, the existence of this second fractional quantum Hall state necessarily implies a gapless phase, which has identical topological response to a noninteracting Weyl semimetal, but is distinct from it. Here, this may be viewed as a generalization (in a weaker form) of the known duality between a noninteracting two-dimensional Dirac fermion and QED 3 to 3+1 dimensions. In addition we discuss a (3+1)-dimensional topologically ordered state, obtained by gapping a nodal line semimetal without breaking symmetries.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Topological Hall Effect Anisotropy in Kagome Bilayer Metal Fe 3 Sn 2

Kagome lattice materials attract growing interest for their topological properties and flat-bands in electronic structure. We present comprehensive study on the anisotropy and out-of-plane electric transport in Fe 3 Sn 2 , a metal with bilayer of Fe kagome planes and with massive Dirac fermions that features high-temperature non-collinear magnetic structure and magnetic skyrmions. For the electrical current path along the c-axis, in micron-size crystals, we found a large topological Hall effect (THE) over a wide temperature range down to spin-glass state. Twofold and fourfold angular magnetoresistance are observed for different magnetic phases, reflecting the competition of magnetic interactions and magnetic anisotropy in kagome lattice that preserve robust topological Hall effect for inter kagomebilayer currents. This information provides new insight into the anisotropy in Fe 3 Sn 2 , of interest in skyrmionicbubble application-related micron-size devices.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Eureka: Enabling Fine-Grained Access and Range Queries on Compressed Scientific Data via Data-Index Co-Compression

Handling large-scale scientific data in high-performance computing (HPC) environments poses significant challenges, including excessive I/O, high storage costs, and slow query performance. Traditional approaches often require full data decompression and scans, making them impractical for real-time or interactive analysis. To address these limitations, we introduce Eureka, a unified data-index co-compression framework that enables fine-grained access and efficient range queries on compressed scientific datasets. Eureka integrates spatial domain decomposition with block-wise error-bounded lossy compression to support selective decompression. It constructs a hierarchical AVL-tree index during compression to capture block-level value ranges, enabling fast pruning during query execution. To reduce metadata overhead, the index itself is also compressed while ensuring recall-preserving results. Experiments on six diverse HPC simulation datasets show that Eureka achieves up to 25x data compression and over 300x index compression, surpassing state-of-the-art compressors such as SZ3 and ZFP in rate-distortion performance. Additionally, Eureka delivers over 30x speedup for low-selectivity range queries, making it a scalable and efficient solution for modern scientific data analysis.

Yan, Ning↗
DOE OSTI2023

Phase diagram of twisted bilayer graphene at filling factor $v = ±3$

Here, we study the correlated insulating phases of twisted bilayer graphene (TBG) in the absence of lattice strain at integer filling $v = ±3$. Using the self-consistent Hartree-Fock method on a particle-hole symmetric model and allowing translation symmetry breaking terms, we obtain the phase diagram with respect to the ratio of AA interlayer hopping (w 0 ) and AB interlayer hopping (w 1 ). When the interlayer hopping ratio is close to the chiral limit (w 0 /w 1 ≲ 0.5), a quantum anomalous Hall state with Chern number v c = ±1 can be observed consistent with previous studies. Around the realistic value w 0 /w 1 ≈ 0.8, we find a spin and valley polarized, translation symmetry breaking, state with C 2⁢z T symmetry, a charge gap and a doubling of the moiré unit cell, dubbed theC 2⁢z T stripe phase. The real-space total charge distribution of this C 2⁢z T stripe phase in the flat band limit does not have modulation between different moiré unit cells, although the charge density in each layer is modulated, and the translation symmetry is strongly broken. Other symmetries, including C 2⁢z , C 2⁢x , particle-hole symmetry P, and the topology of the C 2⁢z T stripe phase, are also discussed in detail. We observed braiding and annihilation of the Dirac nodes by continuously turning on the order parameter to its fully self-consistent value, and provide a detailed explanation of the mechanism for the charge gap opening despite preserving C 2⁢z T and valley U⁡(1) symmetries. In the transition region between the quantum anomalous Hall phase and the C 2⁢z T stripe phase, we find an additional competing state with comparable energy corresponding to a phase with a tripling of the moiré unit cell.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗