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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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NASA NTRS2009

The Supercritical Pile GRB Model: The Prompt to Afterglow Evolution

The "Supercritical Pile" is a very economical GRB model that provides for the efficient conversion of the energy stored in the protons of a Relativistic Blast Wave (RBW) into radiation and at the same time produces - in the prompt GRB phase, even in the absence of any particle acceleration - a spectral peak at energy approx. 1 MeV. We extend this model to include the evolution of the RBW Lorentz factor Gamma and thus follow its spectral and temporal features into the early GRB afterglow stage. One of the novel features of the present treatment is the inclusion of the feedback of the GRB produced radiation on the evolution of Gamma with radius. This feedback and the presence of kinematic and dynamic thresholds in the model can be the sources of rich time evolution which we have began to explore. In particular. one can this may obtain afterglow light curves with steep decays followed by the more conventional flatter afterglow slopes, while at the same time preserving the desirable features of the model, i.e. the well defined relativistic electron source and radiative processes that produce the proper peak in the (nu)F(sub nu), spectra. In this note we present the results of a specific set of parameters of this model with emphasis on the multiwavelength prompt emission and transition to the early afterglow.

Mastichiadis, A.↗

NASA NTRS2010

A Bulk Comptonization Model for the Prompt GRM Emission

The "Supercritical Pile" is a very economical GRB model that provides for the efficient conversion of the energy stored in the protons of a Relativistic Blast Wave (RBW) into radiation and at the same time produces - in the prompt GRB phase, even in the absence of any particle acceleration - a spectral peak at energy approximately 1 MeV. We extend this model to include the evolution of the RBW Lorentz factor F and thus follow its spectral and temporal features into the early GRB afterglow stage. One of the novel features of the present treatment is the inclusion of the feedback of the GRB produced radiation on the evolution of Gamma with radius. This feedback and the presence of kinematic and dynamic thresholds in the model are sources of potentially very rich time evolution which we have began to explore. In particular, one can this way obtain afterglow light curves with steep decays followed by the more conventional flatter afterglow slopes, while at the same time preserving the desirable features of the model, i.e. the well defined relativistic electron source and radiative processes that produce the proper peak in the nu F(sub nu) spectra. In this note we present the results of a specific set of parameters of this model with emphasis on the multiwavelength prompt emission and transition to the early afterglow.

Kazanas, Demos↗

DOE OSTI2026

Enhancing dimensionality prediction in hybrid metal halides via feature engineering and class-imbalance mitigation

We present a machine learning (ML) framework for predicting the structural dimensionality of hybrid metal halides (HMHs), including organic-inorganic perovskites, using a combination of chemically-informed feature engineering and advanced class-imbalance handling techniques. This study is motivated by the small and highly imbalanced nature of experimentally available HMH datasets, which limits the applicability and reliability of conventional ML approaches. The dataset, consisting of 494 HMH structures, is highly imbalanced across dimensionality classes (0D, 1D, 2D, 3D), posing significant challenges to predictive modeling. To mitigate this limitation, the dataset was augmented to 1336 samples using the synthetic minority oversampling technique, enabling improved learning of underrepresented dimensionality classes while preserving chemically meaningful feature relationships. We developed interaction-based descriptors designed to capture coupled steric and polarity effects relevant to dimensionality prediction, which are not readily captured by standard single-parameter or composition-only descriptors. These descriptors are integrated into a multi-stage workflow combining feature selection, ensemble stacking, and performance optimization. Our approach significantly improves F1-scores for underrepresented classes, achieving robust cross-validation performance across all dimensionalities. This work demonstrates a generalizable strategy for extracting reliable and interpretable structure–dimensionality relationships from limited experimental data, enabling pre-synthesis screening of organic cations and providing a practical blueprint for small-data ML in hybrid materials systems.

36 MATERIALS SCIENCE↗

NASA NTRS1985

Minimal norm constrained interpolation

In computational fluid dynamics and in CAD/CAM, a physical boundary is usually known only discreetly and most often must be approximated. An acceptable approximation preserves the salient features of the data such as convexity and concavity. In this dissertation, a smooth interpolant which is locally concave where the data are concave and is locally convex where the data are convex is described. The interpolant is found by posing and solving a minimization problem whose solution is a piecewise cubic polynomial. The problem is solved indirectly by using the Peano Kernal theorem to recast it into an equivalent minimization problem having the second derivative of the interpolant as the solution. This approach leads to the solution of a nonlinear system of equations. It is shown that Newton's method is an exceptionally attractive and efficient method for solving the nonlinear system of equations. Examples of shape-preserving interpolants, as well as convergence results obtained by using Newton's method are also shown. A FORTRAN program to compute these interpolants is listed. The problem of computing the interpolant of minimal norm from a convex cone in a normal dual space is also discussed. An extension of de Boor's work on minimal norm unconstrained interpolation is presented.

Irvine, L. D.↗

DOE OSTI2024

Small‐Molecule Mixed Ionic‐Electronic Conductors for Efficient N‐Type Electrochemical Transistors: Structure‐Function Correlations

Abstract The fundamental challenge in electron‐transporting organic mixed ionic‐electronic conductors (OMIECs) is simultaneous optimization of electron and ion transport. Beginning from Y6‐type/U‐shaped non‐fullerene solar cell acceptors, we systematically synthesize and characterize molecular structures that address the aforementioned challenge, progressively introducing increasing numbers of oligoethyleneglycol (OEG; g) sidechains from 1 g to 3 g, affording OMIECs 1gY, 2gY, and 3gY, respectively. The crystal structure of 1gY preserves key structural features of the Y n series: a U‐shaped/planar core, close π–π molecular stacking, and interlocked acceptor groups. Versus inactive Y6 and Y11, all of the new glycolated compounds exhibit mixed ion‐electron transport in both conventional organic electrochemical transistor (cOECT) and vertical OECT (vOECT) architectures. Notably, 3gY with the highest OEG density achieves a high transconductance of 16.5 mS, an on/off current ratio of ~10 6 , and a turn‐on/off response time of 94.7/5.7 ms in vOECTs. Systematic optoelectronic, electrochemical, architectural, and crystallographic analysis explains the superior 3gY‐based OECT performance in terms of denser n gY OEG content, increased crystallite dimensions with decreased long‐range crystalline order, and enhanced film hydrophilicity which facilitates ion transport and efficient redox processes. Finally, we demonstrate an efficient small‐molecule‐based complementary inverter using 3gY vOECTs, showcasing the bioelectronic applicability of these new small‐molecule OMIECs.

Cho, Yongjoon↗

DOE OSTI2024

Small‐Molecule Mixed Ionic‐Electronic Conductors for Efficient N‐Type Electrochemical Transistors: Structure‐Function Correlations

Cho, Yongjoon↗

DOE OSTI2025

3D mesh regularization within an ALE code using a weighted line sweeping method

The Lagrangian formalism is widely used to simulate hydrodynamic responses in complex engineering applications, particularly those involving strong shock waves. However, as the mesh moves with the fluid, it can become highly distorted, requiring a regularization step. This involves constructing a new grid and remapping conservative quantities onto it to restore mesh quality. This work introduces a regularization method for block-structured meshes within a 3D ALE (Arbitrary Lagrangian-Eulerian) code. The proposed approach prevents mesh tangling while preserving the anisotropic features of the initial Lagrangian mesh. This regularization technique incorporates aspect ratio-based weights to control mesh smoothing. Unlike uniform rezoning techniques, this weighted approach maintains proximity to the Lagrangian mesh while improving mesh quality. Here, the method effectively handles concave geometries by mitigating the grid attraction phenomenon, which typically leads to mesh concentration along concave edges. Numerical experiments demonstrate its efficiency in regularizing severely deformed meshes, and its integration within the ALE framework is validated on challenging hydrodynamic test cases, including the triple point problem.

42 ENGINEERING↗

DOE OSTI2022

KSSOLV 2.0: An efficient MATLAB toolbox for solving the Kohn-Sham equations with plane-wave basis set

KSSOLV (Kohn-Sham Solver) is a MATLAB toolbox for performing Kohn-Sham density functional theory (DFT) calculations with a plane-wave basis set. KSSOLV 2.0 preserves the design features of the original KSSOLV software to allow users and developers to easily set up a problem and perform ground-state calculations as well as to prototype and test new algorithms. Furthermore, it includes new functionalities such as new iterative diagonalization algorithms, k-point sampling for electron band structures, geometry optimization and advanced algorithms for performing DFT calculations with local, semi-local, and hybrid exchange-correlation functionals. It can be used to study the electronic structures of both molecules and solids. So, we describe these new capabilities in this work through a few use cases. We also demonstrate the numerical accuracy and computational efficiency of KSSOLV on a variety of examples.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Reinforcement learning based hybrid bond-order coarse-grained interatomic potentials for exploring mesoscale aggregation in liquid–liquid mixtures

Exploring mesoscopic physical phenomena has always been a challenge for brute-force all-atom molecular dynamics simulations. Although recent advances in computing hardware have improved the accessible length scales, reaching mesoscopic timescales is still a significant bottleneck. Coarse-graining of all-atom models allows robust investigation of mesoscale physics with a reduced spatial and temporal resolution but preserves desired structural features of molecules, unlike continuum-based methods. Here, we present a hybrid bond-order coarse-grained forcefield (HyCG) for modeling mesoscale aggregation phenomena in liquid–liquid mixtures. The intuitive hybrid functional form of the potential offers interpretability to our model, unlike many machine learning based interatomic potentials. We parameterize the potential with the continuous action Monte Carlo Tree Search (cMCTS) algorithm, a reinforcement learning (RL) based global optimizing scheme, using training data from all-atom simulations. The resulting RL-HyCG correctly describes mesoscale critical fluctuations in binary liquid–liquid extraction systems. cMCTS, the RL algorithm, accurately captures the mean behavior of various geometrical properties of the molecule of interest, which were excluded from the training set. The developed potential model along with the RL-based training workflow could be applied to explore a variety of other mesoscale physical phenomena that are typically inaccessible to all-atom molecular dynamics simulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

SNNVis: Visualizing Graph Embedding of Evolutionary Optimization for Spiking Neural Networks

While Spiking Neural Networks (SNNs) show a lot of promise, it is difficult to optimize them because applying traditional gradient-based optimization techniques is difficult. Even though evolutionary algorithms (EAs) have been shown to promise to optimize SNNs, understanding the relationship between evolving the characteristics of SNNs and their performance to improve the optimization algorithm is challenging because of the complex characteristics and huge population size. We propose visual analytics with novel graph embedding for evolutionary SNNs to address the challenges. While existing graph embedding techniques have limitations in preserving the specific features of the nodes and edges, our approach maintains them. Also, we develop visual analytics for understanding the relationship between the network performance and the features of nodes and edges and exploring and analyzing the evolving SNNs to build insights into improving the EA.

Chae, Junghoon [ORNL] (ORCID:0000000206016746)↗

DOE OSTI2026

Spatially Accelerated Winding Numbers for Curved Geometry

The generalized winding number (GWN) is a scalar field that supports robust containment queries on curved geometry, including non-watertight, overlapping, and nested boundary representations. While queries can be easily parallelized over samples, direct evaluation on parametric curves and surfaces remains costly for large and complex models. Fast, state-of-the-art GWN approaches leverage a spatial index to approximate the GWN, typically coupled with a Taylor expansion which approximates the GWN contribution for far clusters of geometric primitives. However, such methods operate only on discrete inputs such as triangle meshes and point clouds, and would introduce containment errors near boundaries if applied to curved input. We extend support for fast GWN evaluation over arbitrary collections of NURBS curves in 2D and trimmed NURBS patches in 3D via a Bounding Volume Hierarchy that stores efficiently precomputed moment data in the hierarchy nodes. When querying the hierarchy, approximations for far clusters are used alongside direct evaluation for nearby NURBS primitives, achieving sub-linear complexity while preserving the geometric features in the vicinity of the query point. Central to our performance improvements is an adaptive subdivision strategy for NURBS primitives during a preprocessing phase, creating better spatial partitions while retaining the same accuracy for containment decisions as a direct evaluation. We demonstrate the performance and accuracy of our approach across a large collection of 2D and 3D datasets.

Computer science↗

DOE OSTI2026

torch-einshard v1.0

torch-einshard is a Python library for describing local and distributed PyTorch tensor computations with compact, einsum-like notation. Its expressions name logical axes, specify how they are sharded across a PyTorch DeviceMesh, and represent partial reductions. The library automatically performs contractions, permutations, reshaping, splitting, gathering, reduction, reduce-scatter, and repartitioning while preserving autograd. Additional features include sharding-aware FFTs, tensor rolls, halo exchange, sliding windows, 1D–3D convolutions, uneven-shard handling, parameter initialization and gradient management, and cost-based execution planning. It is designed for scientific machine learning and large-model workloads, including tensor-, sequence-, and spatial-parallel MLPs, attention, convolutions, and spectral operations. Compared with manually combining torch.einsum and distributed collectives, torch-einshard expresses both the mathematical operation and data placement in one readable formula. This reduces boilerplate and synchronization errors, keeps forward and backward communication consistent, and allows the library to select optimized collective strategies without changing model code.

Morozov, Dmitriy [Lawrence Berkeley National Labor↗

DOE OSTI2026

Cryo-EM visualization of viruses from partially irrigated soils

Viruses are numerically the most abundant forms on Earth, and most are present in soil. Even though viruses are highly abundant in soil and critical to rhizosphere function, visualizing the diverse morphotypes within soil has been challenging. The difficulty is primarily due to the heterogenous nature of isolated suspensions that typically contain nanometer to micron scale debris which renders protein crystallography for structural studies unfeasible and hinders cryo-electron microscopy due to ice thickness and contrast issues. Here we employed and compared a simple spin filtration method to cleanup solutions of extracted viruses for direct observation with cryo-electron microscopy. The method employs common physical biochemical separation steps to remove large and small debris which dramatically improves image quality and preservation of structural features to permit visualizing morphotypes not typically seen with conventional negative stain approaches. In addition to tailed and non-tailed polyhedral phages, several under reported or novel morphotypes of soil viruses are directly visualized as a particle library with both 2D and 3D information.

cryo-EM↗

NASA NTRS1980

Heavy ion composition in solar flares at low energies, 1973-1977

The composition of solar flare particles near 1 MeV/nucleon has been surveyed for the major species between H and Fe over a four-year period using the IMP-8 ULET instrument. The average composition for the larger flares is found to be remarkably similar to that found by previous studies at energies greater than 10 MeV/nucleon. Variations in abundance are found to be well ordered by atomic number. It is suggested that such a smooth variation in abundances is due to an acceleration mechanism which preserves the main features of the ambient composition of the accelerated material, at least in the larger flares.

Mason, G. M.↗

NASA NTRS1986

A simple, objective analysis scheme for scatterometer data

A simple economical objective analysis scheme is devised and tested on real scatterometer data. It is designed to treat dense data such as those of the Seasat A Satellite Scatterometer (SASS) for individual or multiple passes, and preserves subsynoptic scale features. Errors are evaluated with the aid of sampling ('bootstrap') statistical methods. In addition, sensitivity tests have been performed which establish qualitative confidence in calculated fields of divergence and vorticity. The SASS wind algorithm could be improved; however, the data at this point are limited by instrument errors rather than analysis errors. The analysis error is typically negligible in comparison with the instrument error, but amounts to 30 percent of the instrument error in areas of strong wind shear. The scheme is very economical, and thus suitable for large volumes of dense data such as SASS data.

Levy, G.↗

NASA NTRS1989

Analytical electron microscopy of biogenic and inorganic carbonates

In the terrestrial sedimentary environment, the mineralogically predominant carbonates are calcite-type minerals (rhombohedral carbonates) and aragonite-type minerals (orthorhombic carbonates). Most common minerals precipitating either inorganically or biogenically are high magnesium calcite and aragonite. High magnesium calcite (with magnesium carbonate substituting for more than 7 mole percent of the calcium carbonate) is stable only at temperatures greater than 700 C or thereabouts, and aragonite is stable only at pressures exceeding several kilobars of confining pressure. Therefore, these carbonates are expected to undergo chemical stabilization in the diagenetic environment to ultimately form stable calcite and dolomite. Because of the strong organic control of carbonate deposition in organisms during biomineralization, the microchemistry and microstructure of invertebrate skeletal material is much different than that present in inorganic carbonate cements. The style of preservation of microstructural features in skeletal material is therefore often quite distinctive when compared to that of inorganic carbonate even though wholesale recrystallization of the sediment has taken place. Microstructural and microchemical comparisons are made between high magnesium calcite echinoderm skeletal material and modern inorganic high magnesium calcite inorganic cements, using analytical electron microscopy and related techniques. Similar comparisons are made between analogous materials which have undergone stabilization in the diagenetic environment. Similar analysis schemes may prove useful in distinguishing between biogenic and inorganic carbonates in returned Martian carbonate samples.

Blake, David F.↗

NASA NTRS1992

Amazonis and Utopia Planitiae: Martian Lacustrine basins

Amazonis and Utopia Planitiae are two large (greater than 10(exp 6) sq. km) basins on Mars having morphological features commonly associated with former lakes. The investigation of these areas is an extension of our previous paleolake studies in the Elysium basin. Using Viking images, we are searching for familiar geologic forms commonly associated with standing bodies of water on Earth. Like Elysium, the two basins exhibit terraces and lineations resembling shorelines, etched and infilled floors with channel-like sinuous markings in places, inflow channels along their borders, and other geomorphic indicators believed to be related to the presence of water and ice. In some areas these features are better displayed than in others where they may be very tenuous; their value as indicators can be justified only by their association with related features. Even though these postulated paleolakes are very young in the Martian stratigraphic sequence, their shoreline features are poorly preserved and they are probably much older than large Pleistocene lakes on Earth.

Scott, David H.↗

NASA NTRS1993

Tests of smoothing methods for topological study of galaxy redshift surveys

Studying the topology of large-scale structure as a way to better understand initial conditions has become more widespread in recent years. Studying topology of simulations (which have periodic boundary conditions) in redshift space produces results compatible with the real topological characteristics of the simulation. Thus we expect we can extract useful information from redshift surveys. However, with nonperiodic boundary conditions, the use of smoothing must result in the loss of information at survey boundaries. In this paper, we test different methods of smoothing samples with nonperiodic boundary conditions to see which most efficiently preserves the topological features of the real distribution. We find that a smoothing method which (unlike most previous published analysis) sums only over cells inside the survey volume produces the best results among the schemes tested.

Melott, Adrian L.↗