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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

ChemPix: automated recognition of hand-drawn hydrocarbon structures using deep learning

Inputting molecules into chemistry software, such as quantum chemistry packages, currently requires domain expertise, expensive software and/or cumbersome procedures. Leveraging recent breakthroughs in machine learning, we develop ChemPix: an offline, hand-drawn hydrocarbon structure recognition tool designed to remove these barriers. A neural image captioning approach consisting of a convolutional neural network (CNN) encoder and a long short-term memory (LSTM) decoder learned a mapping from photographs of hand-drawn hydrocarbon structures to machine-readable SMILES representations. We generated a large auxiliary training dataset, based on RDKit molecular images, by combining image augmentation, image degradation and background addition. Additionally, a small dataset of ~600 hand-drawn hydrocarbon chemical structures was crowd-sourced using a phone web application. These datasets were used to train the image-to-SMILES neural network with the goal of maximizing the hand-drawn hydrocarbon recognition accuracy. By forming a committee of the trained neural networks where each network casts one vote for the predicted molecule, we achieved a nearly 10 percentage point improvement of the molecule recognition accuracy and were able to assign a confidence value for the prediction based on the number of agreeing votes. The ensemble model achieved an accuracy of 76% on hand-drawn hydrocarbons, increasing to 86% if the top 3 predictions were considered.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

IIA Incremental Interval Assignment

IIA is a solver for optimizing integer matrix problems Ax=b. It was developed to decide the number of mesh edges on model curves (intervals) for quad and hex meshing. Meshing schemes impose constraints ranging from the mild requirement (i.e. that any quad mesh must have an even number of edges on its boundary) to structured mapped patches where opposite sides of a rectangle must have exactly equal numbers of edges. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. SAND2020-13618 M

MItchell, Scott↗
DOE OSTI2025

Global River Topology (GRIT): A Bifurcating River Hydrography

Existing global river networks underpin a wide range of hydrological applications but do not represent channels with divergent river flows (bifurcations, multi‐threaded channels, canals), as these features defy the convergent flow assumption that elevation‐derived networks (e.g., HydroSHEDS, MERIT Hydro) are based on. Yet, bifurcations are important features of the global river drainage system, especially on large floodplains and river deltas, and are also often found in densely populated regions. Here we developed the first raster and vector‐based Global RIver Topology that not only represents the tributaries of the global drainage network but also the distributaries, including multi‐threaded rivers, canals and deltas. We achieve this by merging a 30 m Landsat‐based river mask with elevation‐generated streams to ensure a homogeneous drainage density outside of the river mask for rivers narrower than approximately 30 m. Crucially, we employ the new 30 m digital terrain model, FABDEM, based on TanDEM‐X, which shows greater accuracy over the traditionally used SRTM derivatives. After vectorization and pruning, directionality is assigned by a series of elevation, flow angle and continuity approaches. The new global network and its attributes are validated using gauging stations, comparison with existing networks, and randomized manual checks. The new network represents 19.6 million km of streams and rivers with drainage areas greater than 50 km 2 and includes 67,495 bifurcations. With the advent of hyper‐resolution modeling and artificial intelligence, GRIT is expected to greatly improve the accuracy of many river‐based applications such as flood forecasting, water availability and quality simulations, or riverine habitat mapping.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Structural Propensities in Cs2MBiX6 (M=Na, Ag; X=Cl, Br) Bismuth Halide Double Perovskites

A previously unreported low-temperature phase transition in the bismuth halide double perovskite Cs2AgBiCl6 is reported, thereby establishing trends in the structural ground state across Cs2NaBiCl6, Cs2AgBiCl6, and Cs2AgBiBr6. Using the combined toolkit of variable-temperature synchrotron X-ray and neutron powder diffraction, Raman spectroscopy, and density-functional theory–based electronic structure modeling, we demonstrate a cubic Fm¯3m → tetragonal I4/m transition upon cooling with distinct onset temperatures. Neutron powder diffraction refinements permit the unambiguously assignment of the low-temperature phase of Cs2NaBiCl6 to I4/m, correcting prior reports of an I4/mmm ground state. Cs2AgBiCl6 is also found to transforms to a structure crystallizing in the I4/m space group at low temperatures. Temperaturedependent Raman data and density-functional theory-based modeling capture the softening and freezing of out-of-phase octahedral-tilt modes and quantify relative instabilities. Solid-state nuclear magnetic resonance spectroscopy at room temperature completes the characterization and helps underpin the subtle differences in covalency across the compounds. Trends in the phase transition temperature Ts and tilt magnitudes emerge from coupled effects of halide identity, M(I)–site bonding character, and a mismatch between interatomic distances. These results establish the structure– dynamics–bonding framework for tuning tilt-driven instabilities in halide double perovskites.

Tian, Haowen↗
DOE OSTI2025

Replace Human Intelligence with Fast and Smart Geometric Reasoning and Graph Neural Network to Accelerate Next Gen ModSim Workflows

We present an agent-guided approach to CAD geometry decomposition that automates hex/hybrid meshing with graph neural networks (GNNs) to accelerate next-generation ModSim workflows. Our end-to-end pipeline (i) reduces 3D boundary-representation (B-Rep) models to a 2D chordal axis skeleton (CAT) and then to a 1D bipartite graph of surface and curve nodes, (ii) assigns per node labels as Cubit® WebCut actions, (iii) trains a multi-action GNN under supervised learning, and (iv) predicts five surface-node and three curve-node actions on out-of-distribution test geometries. Each graph node carries geometric, topological, and meshing attributes drawn from the B-Rep “skin” and CAT “skeleton,” with two-way mappings across 3D↔2D↔1D representations to maintain traceability back to 3D CAD. The supervised learning model exhibits stable convergence of the binary cross-entropy loss and achieves 98.7% accuracy on unseen lattice models. To operationalize decision-making, we rank predicted commands by geometric significance and prototyped the agent-guided workflow through the Cubit® Meshing PowerTool GUI. As a stretch goal, we explore reinforcement learning (RL) to reduce or remove label requirements and to learn policies for action sequences that maximize total reward (e.g., size of hex-meshable regions and resulting hex mesh quality). When all-hex meshing is not feasible, the agent assists in producing hybrid meshes—prioritizing hex in critical regions and transitioning to tetrahedral elements (tets) elsewhere—maintaining fidelity while ensuring robustness. The overarching objective is to replace manual, heuristics-based decomposition with data-driven, reproducible automation, cutting meshing turnaround time by orders of magnitude. We anticipate direct impact on simulation workflows through intelligent, scalable decomposition of complex CAD models into hex-meshable subdomains.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Closing the Loop between In Situ Stress Complexity and EGS Fracture Complexity

We present an agent-guided approach to CAD geometry decomposition that automates hex/hybrid meshing with graph neural networks (GNNs) to accelerate next-generation ModSim workflows. Our end-to-end pipeline (i) reduces 3D boundary-representation (B-Rep) models to a 2D chordal axis skeleton (CAT) and then to a 1D bipartite graph of surface and curve nodes, (ii) assigns per node labels as Cubit® WebCut actions, (iii) trains a multi-action GNN under supervised learning, and (iv) predicts five surface-node and three curve-node actions on out-of-distribution test geometries. Each graph node carries geometric, topological, and meshing attributes drawn from the B-Rep “skin” and CAT “skeleton,” with two-way mappings across 3D↔2D↔1D representations to maintain traceability back to 3D CAD. The supervised learning model exhibits stable convergence of the binary cross-entropy loss and achieves 98.7% accuracy on unseen lattice models. To operationalize decision-making, we rank predicted commands by geometric significance and prototyped the agent-guided workflow through the Cubit® Meshing PowerTool GUI. As a stretch goal, we explore reinforcement learning (RL) to reduce or remove label requirements and to learn policies for action sequences that maximize total reward (e.g., size of hex-meshable regions and resulting hex mesh quality). When all-hex meshing is not feasible, the agent assists in producing hybrid meshes—prioritizing hex in critical regions and transitioning to tetrahedral elements (tets) elsewhere—maintaining fidelity while ensuring robustness. The overarching objective is to replace manual, heuristics-based decomposition with data-driven, reproducible automation, cutting meshing turnaround time by orders of magnitude. We anticipate direct impact on simulation workflows through intelligent, scalable decomposition of complex CAD models into hex-meshable subdomains.

42 ENGINEERING↗
DOE OSTI2023

Decomposition of liquid-liquid extraction organic phase structure into critical and pre-peak contributions

Solution nanostructure induced by amphiphile self-association plays an important role in various chemical and physical processes, including liquid-liquid extraction (LLE). Small angle scattering techniques are an essential means of probing this structure, but, due to non-uniqueness of scattering patterns, their interpretation is not trivial. Here, in this study, we decompose small angle x-ray scattering (SAXS) patterns for a range of binary LLE organic phases into two components: Ornstein-Zernike and pre-peak contributions resulting from, respectively, critical concentration fluctuations and packing of the amphiphilic extractant molecules in the nonpolar aliphatic diluent. While we previously applied Ornstein-Zernike scattering models to similar organic phases, including the pre-peak explicitly in the fit allows us to measure weak fluctuations at high extractant concentration and simultaneously obtain information on the position and intensity of the correlation peak related to amphiphile packing. Scattering patterns and partial structure factors calculated from molecular dynamics simulations support this interpretation. We demonstrate how this minimal scattering model describes nanostructuring for a large number of extractant types over their entire extractant/diluent composition ranges, suggesting simple and universal behavior. The straightforward assignment of these structural contributions facilitates the comparison of these features between different classes of extractant molecules and will enable further studies of organic phase aggregation. Applicability to more complex, process-relevant organic phases is illustrated with post-contact organic phases containing significant quantities of extracted lanthanide nitrate salt, which we find are readily described by this model.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2026

Dataset for Blueprinting Electrified Transit System Implementation

This dataset contains the figures and tabulated results generated from a system-level optimization study of transit fleet electrification planning. The dataset does not include executable modeling code required to reproduce the optimization. The dataset includes results for optimized charging infrastructure deployment by location and power level and service block assignments by fuel type, battery capacity selections, and distributed energy resource sizing. It also contains aggregated financial results, capital expenditures, operating cost summaries, net present cost comparisons across scenarios, and quantified air quality impacts. Results are structured to reflect multiple planning scenarios, including heuristic electrification plans, system-optimized configurations, and sensitivity cases with alternative objective weightings. The modeling was developed using publicly available General Transit Feed Specification data from Omnitrans and standardized modeling assumptions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Dataset for Blueprinting Electrified Transit System Implementation

This dataset contains the figures and tabulated results generated from a system-level optimization study of transit fleet electrification planning. The dataset does not include executable modeling code required to reproduce the optimization. The dataset includes results for optimized charging infrastructure deployment by location and power level and service block assignments by fuel type, battery capacity selections, and distributed energy resource sizing. It also contains aggregated financial results, capital expenditures, operating cost summaries, net present cost comparisons across scenarios, and quantified air quality impacts. Results are structured to reflect multiple planning scenarios, including heuristic electrification plans, system-optimized configurations, and sensitivity cases with alternative objective weightings. The modeling was developed using publicly available General Transit Feed Specification data from Omnitrans and standardized modeling assumptions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Raman and infrared spectra of plutonium (IV) oxalate and its thermal degradation products

For over 80 years, plutonium dioxide has been routinely produced via thermal decomposition of hydrated plutonium(IV) oxalate. Despite the longstanding utility of this process, the chemical structures of starting materials and intermediates produced during this thermal conversion remain ill-defined. To help resolve this uncertainty, we measured high-resolution Raman and infrared spectra of Pu(C 2 O 4 ) 2 ·6H 2 O that was heated to 25, 100, 220, 250, 350, and 450 °C in air. Our measurements show that Pu(C 2 O 4 ) 2 ·6H 2 O has a rich vibrational spectrum with at least 15 Raman bands between 180 cm -1 and 1900 cm -1 and 9 infrared bands between 800 cm -1 and 4000 cm -1 . As Pu(C 2 O 4 ) 2 ·6H 2 O is heated, water is liberated, and the oxalate ligand decomposes to produce plutonium oxycarbide species. When heated to 350 °C or higher, vibrational spectra are consistent with PuO 2 with some residual carbon-containing species. Full vibrational spectra, powder X-ray diffraction, and scanning electron microscopy measurements of Pu(C 2 O 4 ) 2 ·6H 2 O and its thermal degradation products are presented herein along with approximate assignments for observed spectral bands. These data can be used to validate and potentially improve existing computational models that describe the chemical structure of compounds produced during thermal degradation of plutonium (IV) oxalate. Given the utility of plutonium (IV) oxalate in synthesizing plutonium dioxide, these results are expected to provide value in the fields of nuclear fuel processing, nuclear nonproliferation, and nuclear forensics.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2025

Visualizing Stereodynamics in Cold Collisions through Shape Resonance Wavefunctions

Shape resonances in cold collisions are often strongly affected by stereodynamics. These resonances can sometimes be assigned by single-channel quantization along the scattering coordinate. However, sensitive steric control of collision implies strong anisotropy of the interaction potential energy surface, which usually leads to coupling among multiple scattering channels in the strongly interacting region. Hence, the resonances might be the result of quantization in a multidimensional space. Using the cold rotationally inelastic collision between para-H 2 (v 1 = 1, j 1 = 2) and HF (v 2 = 0, j 2 = 0) as an example, we analyze four low-lying shape resonances via diagonalization of the full-dimensional Hamiltonian with a stabilization method. While some resonances can indeed be assigned with a single partial wave, others apparently involve more than one scattering channel. Furthermore, a new model based on these resonance wavefunctions is developed to better understand the stereodynamics through shape resonances in cold collisions.

Collisions↗
DOE OSTI2021

First β-decay spectroscopy of 135 In and new β-decay branches of 134 In

The β decay of the neutron-rich 134 In and 135 In was investigated experimentally in order to provide new insights into the nuclear structure of the tin isotopes with magic proton number Z=50 above the N=82 shell. The β-delayed γ-ray spectroscopy measurement was performed at the ISOLDE facility at CERN, where indium isotopes were selectively laser-ionized and on-line mass separated. Three β-decay branches of 134 In were established, two of which were observed for the first time. Population of neutron-unbound states decaying via γ rays was identified in the two daughter nuclei of 134 In, 134 Sn and 133 Sn, at excitation energies exceeding the neutron separation energy by 1 MeV. The β-delayed one- and two-neutron emission branching ratios of 134 In were determined and compared with theoretical calculations. The β-delayed one-neutron decay was observed to be dominant β-decay branch of 134 In even though the Gamow-Teller resonance is located substantially above the two-neutron separation energy of 134 Sn. Transitions following the β decay of 135 In are reported for the first time, including γ rays tentatively attributed to 135 Sn. In total, six new levels were identified in 134 Sn on the basis of the βγγ coincidences observed in the 134 In and 135 Inβ decays. A transition that might be a candidate for deexciting the missing neutron single-particle 13/2 + state in 133 Sn was observed in both β decays and its assignment is discussed. Experimental level schemes of 134 Sn and 135 Sn are compared with shell-model predictions. Using the fast timing technique, half-lives of the 2 + , 4 + , and 6 + levels in 134 Sn were determined. From the lifetime of the 4 + state measured for the first time, an unexpectedly large B(E2;4 + → 2 + ) transition strength was deduced, which is not reproduced by the shell-model calculations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Physical-mass calculation of ρ ( 770 ) and K * ( 892 ) resonance parameters via π π and K π scattering amplitudes from lattice QCD

We present our study of the ρ ( 770 ) and K * ( 892 ) resonances from lattice quantum chromodynamics (QCD) employing domain-wall fermions at physical quark masses. We determine the finite-volume energy spectrum in various momentum frames and obtain phase-shift parametrizations via the Lüscher formalism and as a final step the complex resonance poles of the π π and K π elastic scattering amplitudes via an analytical continuation of the models. By sampling a large number of representative sets of underlying energy-level fits, we also assign a systematic uncertainty to our final results. This is a significant extension to data-driven analysis methods that have been used in lattice QCD to date, due to the two-step nature of the formalism. Our final pole positions, M + i Γ / 2 , with all statistical and systematic errors exposed, are M K * = 893 ( 2 ) ( 8 ) ( 54 ) ( 2 ) MeV and Γ K * = 51 ( 2 ) ( 11 ) ( 3 ) ( 0 ) MeV for the K * ( 892 ) resonance and M ρ = 796 ( 5 ) ( 15 ) ( 48 ) ( 2 ) MeV and Γ ρ = 192 ( 10 ) ( 28 ) ( 12 ) ( 0 ) MeV for the ρ ( 770 ) resonance. The four differently grouped sources of uncertainties are, in the order of occurrence: statistical, data-driven systematic, an estimation of systematic effects beyond our computation (dominated by the fact that we employ a single lattice spacing), and the error from the scale-setting uncertainty on our ensemble. Published by the American Physical Society 2025

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

ccp137/SUGAR

This package (SUrface-wave Grader with ARtificial intelligence or SUGAR) automatically assigns a quality score to surface-wave seismograms (SAC format) using a trained artificial neural network model (included). Specifically, the python script 01_apply_ann.py calculates probability scores for a list of SAC files. You may consider seismograms with probability scores larger than 0.5 as acceptable data. Note no scores will be given to seismograms that do not pass an initial check (e.g., insufficient number of data points). See https://doi.org/10.1002/essoar.10507941.3 for more details.

Chai, Chengping [Oak Ridge National Lab. (ORNL), O↗
DOE OSTI2025

Sensitivity calibration of a Carestream HPX-1 image plate scanner

There is a need to measure the sensitivity of the image plate scanners used to scan Z shot data. The Carestream HPX-1 image plate scanner was tested, and its performance was characterized to determine the system sensitivity in terms of signal per incident photon as a function of x-ray energy. A Manson source was used to simultaneously expose an Amptek x-ray multi-channel analyzer and image plates, allowing for a comparison of the counts as a function of energy to the signal recorded on the image plates. NIST-certified radioactive sources were used to assign an absolute sensitivity. Results indicate that the HPX-1 scanner response matches the shape of the modeled response, allowing for absolute scaling using NIST-certified radioisotope sources. The HPX-1 scanner shows a stable response with measurements taken over one week. In contrast, the sensitivity of the DITABIS scanner was characterized using the same approach, but it exhibits changes in its response that varied by more than 50% over the span of 4 days.

42 ENGINEERING↗
DOE OSTI2024

Geophysical and Environmental Monitoring Data, and Subsurface Flow Modelling Results for Chicken Bone Meadow, Mt. Snodgrass, Crested Butte, CO

This dataset includes geoelectrical monitoring data acquired between October 2021 and November 2022, soil moisture and temperature data, groundwater data obtained from borehole SNIB covering the period from June 2021 to September 2022, and hydrological modelling results. The data were acquired to investigate how variations in bedrock type and topography, and vegetation cover control subsurface flow dynamics. To provide insights into the subsurface flow dynamics and their controls, a monitoring transect was installed at the Chicken Bone Meadow, Mt. Snodgrass, Crested Butte, CO, measuring the spatio-temporal variations of soil moisture, soil and snow temperature, subsurface electrical resistivity variations, and groundwater dynamics. Field data are organized in a folder structure, with Electrical Resistivity Tomography (ERT) data being provided as one file per measurement, and data of the soil moisture and temperature sensors being provided as text files covering the entire monitoring period. The ‘Locations.csv’ file contains the location of all sensors, given in NAD83 – UTM Zone 13N. ERT monitoring data has been processed to filter data based on reciprocal errors (data with errors > 30% were removed), a linear error model was fitted to each survey, and to ensure a constant set of measurements for time-lapse inversion, filtered data were interpolated and assigned a 100% measurement error. Soil moisture and temperature data were acquired at 15 min intervals, and averaged to provide 1h data. Weather data and borehole data (groundwater depth, conductivity and temperature) were acquired at 30 min intervals, and are provided as daily measurements; all measurements are averaged, except of precipitation values, which are given as daily accumulation. The hydrological model was set up along the ERT monitoring transect, and net infiltration was used as surface boundary condition and derived from the weather data. Four different results are provided, (1) results for a parameterization using hydraulic permeability and porosity as derived from the ERT data through petrophysical relationships, and (2) three simplified model results, using 1 to 3 geological layers above the bedrock. Modelling was performed using PFLOTRAN, and for each model the PFLOTRAN input files are provided. The result files include weekly hydrological modelling results (e.g., saturation, velocities, pressures), as well as the model parameterization. The dataset additionally includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata; and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Isomers and hindrances in No254: A touchstone for theories of superheavy nuclei

We report on a new spectroscopic study of the decay of high-K isomers in No152102254, a touchstone nucleus for testing models to understand the structure of superheavy nuclei. The experiment, performed using the Argonne gas-filled analyzer (AGFA), was geared toward resolving long-standing ambiguities in spin-parity and configuration assignments for the two- and four-quasiparticle (qp) intrinsic excitations identified in this nucleus. The isomer decay schemes are firmly established with the help of the highest-statistics γ-γ coincidence data collected to date, providing anchor points for competing theories. A newly measured half-life in the nanosecond range establishes a second 2-qp isomer in No254. The preferred decay pathways for the 2- and 4-qp isomers are discussed, providing new insights into the underlying hindrance mechanisms at play in these heavy nuclei. With firm configuration assignments, the intrinsic excitations in this deformed mass region provide stringent constraints and challenge the different theoretical approaches at play in understanding the structure of superheavy nuclei.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Exploring Data Set Bias and Decision Support with Predictive Uncertainty Through Bayesian Approximations and Convolutional Neural Networks

Individual seismic catalogs can contain multiscale observations from fault level to global scales and associated waveforms from discrete events reflect crustal structure across many different scales and locations. Seismic network aperture, geographic location, and observation distance may not provide informative guidance or intuition on how different catalogs will behave across models trained under different conditions. We rely on uncertainty to provide guardrails for when to trust model decisions, but understanding when our uncertainty is trustworthy is an open challenge. Here, in this work, we explore Bayesian approximation methods for assigning predictive uncertainty in seismic event classification problems. We find that computationally expensive Bayesian approximations do not outperform simple ensemble methods. We also find that when exploiting multiple seismic event catalogs, joint training with data from all the catalogs combined with Bayesian approximations and supervised training for classification can obscure bias and result in less robust uncertainty while also not providing substantial performance benefits compared to training individual models for each catalog.

58 GEOSCIENCES↗