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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Pavement condition and climatic data in southeast Texas: A dataset for evaluating flood impacts on pavement performance

Effective pavement maintenance is essential for economic stability, optimal network performance, and roadway safety. Achieving this requires thorough evaluation of pavement conditions, including structural integrity, surface roughness, and distress characteristics. Pavement performance indicators play a critical role in influencing vehicle safety and ride quality. Recent advances have emphasized the use of data-driven modeling to anticipate pavement behavior, with the goal of optimizing resource allocation and refining Maintenance and Rehabilitation (M&R) strategies through accurate condition assessment. A foundational requirement for these modeling efforts is the availability of standardized, high-quality datasets that can support robust and reproducible infrastructure analysis. This data article presents a comprehensive dataset assembled to facilitate pavement performance prediction, with a geographic focus on Southeast Texas, particularly the flood-vulnerable area of Beaumont. The dataset encompasses pavement and traffic attributes, meteorological records, flood simulation outputs, ground deformation measurements, and topographic indices, enabling detailed examination of both load-associated and non-load-associated degradation mechanisms. Data preprocessing was performed using ArcGIS Pro, Microsoft Excel, and Python to ensure consistency and usability in data-driven modeling applications, including machine learning workflows. Key contributions of this dataset include its utility in analyzing the climatic and environmental factors affecting pavement conditions, identifying critical predictive features, and enabling in-depth correlation analysis across diverse variables. By filling existing gaps in input variable selection resources, this dataset supports the development of predictive tools for estimating future maintenance demand and enhancing the resilience of pavement networks in flood-impacted areas. The resource highlights the importance of standardized datasets for advancing pavement management practices and provides a robust foundation for ongoing infrastructure performance modeling.

42 ENGINEERING↗
DOE OSTI2020

Enhancing nuclear data validation analysis by using machine learning

In this work, we demonstrate how machine learning (ML) techniques can be used as an auxiliary tool for nuclear data validation analysis. The output of the ML analysis can inform evaluators and validators on the quality, or lack thereof, of specific nuclear data and benchmark experiments with respect to simulating these experimental benchmark values. To this end, measured and simulated effective neutron multiplication factors, k eff , of 875 selected ICSBEP critical assemblies and the latter's sensitivities with respect to nuclear data as well as benchmarks' features (e.g., material nuclide, core geometry) are used as input for a random forest (RF) regression model. The RF is used to encode the complex inter-dependencies between thousands of nuclear data features (i.e., sensitivity profiles and aspects describing the measurements) and 875 simulated and experimental k eff values in order to understand what nuclear data features are most informative for predicting bias. The complexity of relationships and high-dimensional space is difficult-to-impossible to search fully using simply expert judgment. As a first proof-of-concept—step, it is demonstrated that this technique is able to correctly trace large discrepancies between simulated and experimental k eff back to fabricated shortcomings in nuclear data that were perturbed to simulated k eff values. In a second, real-case scenario, step, the RF algorithm is used to validate the ENDF/B-VIII.0 library in comparison to ENDF/B-VII.1 nuclear data. One case is showcased where the chosen ML algorithms highlighted nuclear data (the 19 F(n,inl) cross section from 0.4–0.9 MeV) that are shown to be problematic by comparing them to associated differential experimental data and nuclear data from other libraries. In addition to that, it is shown that the RF results point towards poor benchmark experiments and associated underestimated uncertainties (e.g., the PU-SOL-THERM-028 series). However, using the RF algorithm for validating nuclear data with respect to k eff is currently limited to pinpointing groups of questionable nuclear data due to the inherent correlations between features introduced by the nuclear data themselves and how k eff is simulated. Due to this, we recommend that the ML methods presented be used to augment—rather than replace—the expert knowledge of evaluators and validators.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2025

MS25: Materials Science-Focused Benchmark Data Set for Machine Learning Interatomic Potentials

Here, we present MS25, a benchmark data set for evaluating machine learning interatomic potentials (MLIPs) across diverse materials-relevant systems including MgO surfaces, liquid water, zeolites, a catalytic Pt surface reaction, high-entropy alloys (HEAs), and disordered Zr-oxides. Five MLIP architectures (MACE, NequIP, Allegro, MTP, and Torch-ANI) are trained and tested, focusing not only on traditional metrics (energies, forces, and stresses) but also explicitly validating derived physical observables such as lattice constants, volumes, and reaction barriers. We find that most models reach comparable accuracy on standard error metrics across the simple systems, although equivariant MLIPs offer 1.5–2× improvements over nonequivariant MLIPs in energy and force error for structurally complex or compositionally disordered environments such as HEAs and Zr–O systems. Our analysis highlights that low errors in energy and force predictions do not guarantee reliable observables, emphasizing the necessity of explicit validation. We demonstrate limitations in cross-framework transferability, as models trained on one zeolite framework (CHA) fail to reliably generalize to predictions of structurally distinct frameworks (e.g., MFI). Size-extensive tests show some dependence on system size for MgO, resulting from forced periodicity. The HEA and Zr–O data sets are identified as challenging tests for future benchmarks and MLIP model architecture developments as they show significant differentiation in error between MLIP architectures and are still relatively difficult at 1000 training images. Moving forward, we recommend that benchmarking efforts shift their focus from marginal accuracy improvements in energy and force errors toward identifying and understanding model failure modes, rigorously assessing transferability, and evaluating how their errors affect observable predictions. For researchers looking to choose an MLIP architecture, we suggest selecting equivariant MLIP architectures if the complexity of the system is a challenge. For simple materials problems, auxiliary features such as integration with molecular dynamics engines, trade-offs between computational data set generation cost vs MLIP inference speed, and framework integration may play a more important decision factor than small differences in error metrics that are unlikely to matter for production-level research.

chemical structure↗
DOE OSTI2020

Emergence of Topologically Nontrivial Spin-Polarized States in a Segmented Linear Chain

The synthesis of new materials with novel or useful properties is one of the most important drivers in the fields of condensed matter physics and materials science. Discoveries of this kind are especially significant when they point to promising future basic research and applications. Van der Waals bonded materials comprised of lower-dimensional building blocks have been shown to exhibit emergent properties when isolated in an atomically thin form. Here, we report the discovery of a transition metal chalcogenide in a heretofore unknown segmented linear chain form, where basic building blocks each consisting of two hafnium atoms and nine tellurium atoms (Hf 2 Te 9 ) are van der Waals bonded end-to-end. First-principle calculations based on density functional theory reveal striking crystal-symmetry-related features in the electronic structure of the segmented chain, including giant spin splitting and nontrivial topological phases of selected energy band states. In this work, atomic-resolution scanning transmission electron microscopy reveals single segmented Hf 2 Te 9 chains isolated within the hollow cores of carbon nanotubes, with a structure consistent with theoretical predictions. Van der Waals-bonded segmented linear chain transition metal chalcogenide materials could open up new opportunities in low-dimensional, gate-tunable, magnetic and topological crystalline systems.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Unconventional Wells Interference: Supervised Machine Learning for Detecting Fracture Hits

The primary objective of the study was development of a machine learning (ML)-based workflow for fracture hit (“frac hit”) detection and monitoring using shale oil-field data such as drilling surveys, production history (oil and produced water), pressure, and fracking start time and duration records. The ML method takes advantage of long short-term memory (LSTM) and multilayer perceptron (MLP) neural networks to identify the frac hits due to hydraulic communication between the fracking child well(s) and the producing parent well(s) within the same pad (intra-pad interaction) and/or on different pads (inter-pad interaction). It utilizes time series of pressure and production data from within a pad and from adjacent pads. The workflow can capture time variable features of frac hits when the model architecture is deep and wide enough, with enough trainable parameters for deep learning and feature extraction, as demonstrated in this paper by using training and testing subsets of the field data from selected neighboring pads with over a couple of hundred wells. The study was focused on frac-hit interaction among paired wells and demonstrated that the ML model, once trained, can predict the frac-hit probability.

58 GEOSCIENCES↗
DOE OSTI2020

Modeling Assessment of Tidal Energy Extraction in the Western Passage

Numerical models have been widely used for the resource characterization and assessment of tidal instream energy. The accurate assessment of tidal stream energy resources at a feasibility or project-design scale requires detailed hydrodynamic model simulations or high-quality field measurements. This study applied a three-dimensional finite-volume community ocean model (FVCOM) to simulate the tidal hydrodynamics in the Passamaquoddy–Cobscook Bay archipelago, with a focus on the Western Passage, to assist tidal energy resource assessment. IEC Technical specifications were considered in the model configurations and simulations. The model was calibrated and validated with field measurements. Energy fluxes and power densities along selected cross sections were calculated to evaluate the feasibility of the tidal energy development at several hotspots that feature strong currents. When taking both the high current speed and water depth into account, the model results showed that the Western Passage has great potential for the deployment of tidal energy farms. The maximum extractable power in the Western Passage was estimated using the Garrett and Cummins method. Different criteria and methods recommended by the IEC for resource characterization were evaluated and discussed using a sensitivity analysis of energy extraction for a hypothetical tidal turbine farm in the Western Passage.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

REFRACTORY COMPACT HEAT EXCHANGERS WITH EMBEDDED SENSORS ENABLED BY HYBRID ADVANCED SINTERING AND ADDITIVE APPROACH

Structural health monitoring (SHM) of compact heat exchangers (CHXs) operating in extreme environments is essential for ensuring system reliability, safety, and longevity. This study presents the development of high-temperature sensors fabricated via aerosol jet printing (AJP) using platinum ink, selected for its exceptional thermal stability, oxidation resistance, and electrical conductivity. AJP enables precise deposition of fine-feature sensor patterns onto complex geometries, making it well-suited for integration within CHX architectures. To enhance sensor durability, an alumina-based ceramic protective layer was printed over the platinum sensing elements. The sensors demonstrated stable, repeatable performance up to 900?°C during extended thermal cycling. A custom test setup was developed to evaluate sensor accuracy and robustness under steady-state and transient conditions. Substrate screening identified HG-1 ceramic-coated stainless steel as the most effective platform, offering strong adhesion and low resistance. Furthermore, electric field-assisted sintering (EFAS) was employed to embed the sensors into stainless steel 316L matrices without degrading their functionality. Post-embedding electrical tests confirmed sensor integrity, and initial characterization suggests strong potential for in-situ monitoring. This work provides a scalable strategy for integrating high-performance temperature sensors directly into refractory components, advancing embedded SHM technologies for harsh operating environments.

36 - MATERIALS SCIENCE↗
DOE OSTI2024

Human limits in machine learning: prediction of potato yield and disease using soil microbiome data

Abstract Background The preservation of soil health is a critical challenge in the 21st century due to its significant impact on agriculture, human health, and biodiversity. We provide one of the first comprehensive investigations into the predictive potential of machine learning models for understanding the connections between soil and biological phenotypes. We investigate an integrative framework performing accurate machine learning-based prediction of plant performance from biological, chemical, and physical properties of the soil via two models: random forest and Bayesian neural network. Results Prediction improves when we add environmental features, such as soil properties and microbial density, along with microbiome data. Different preprocessing strategies show that human decisions significantly impact predictive performance. We show that the naive total sum scaling normalization that is commonly used in microbiome research is one of the optimal strategies to maximize predictive power. Also, we find that accurately defined labels are more important than normalization, taxonomic level, or model characteristics. ML performance is limited when humans can’t classify samples accurately. Lastly, we provide domain scientists via a full model selection decision tree to identify the human choices that optimize model prediction power. Conclusions Our study highlights the importance of incorporating diverse environmental features and careful data preprocessing in enhancing the predictive power of machine learning models for soil and biological phenotype connections. This approach can significantly contribute to advancing agricultural practices and soil health management.

Aghdam, Rosa↗
DOE OSTI2022

The ecological assembly of bacterial communities in Antarctic wetlands varies across levels of phylogenetic resolution

Summary As functional traits are conserved at different phylogenetic depths, the ability to detect community assembly processes can be conditional on the phylogenetic resolution; yet most previous work quantifying their influence has focused on a single level of phylogenetic resolution. Here, we have studied the ecological assembly of bacterial communities from an Antarctic wetland complex, applying null models across different levels of phylogenetic resolution (i.e. clustering ASVs into OTUs with decreasing sequence identity thresholds). We found that the relative influence of the community assembly processes varies with phylogenetic resolution. More specifically, selection processes seem to impose stronger influence at finer (100% sequence similarity ASV) than at coarser (99%–97% sequence similarity OTUs) resolution. We identified environmental features related with the ecological processes and propose a conceptual model for the bacterial community assembly in this Antarctic ecosystem. Briefly, eco‐evolutionary processes appear to be leading to different but very closely related ASVs in lotic, lentic and terrestrial environments. In all, this study shows that assessing community assembly processes at different phylogenetic resolutions is key to improve our understanding of microbial ecology. More importantly, a failure to detect selection processes at coarser phylogenetic resolution does not imply the absence of such processes at finer resolutions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Towards automating structural discovery in scanning transmission electron microscopy *

Abstract Scanning transmission electron microscopy is now the primary tool for exploring functional materials on the atomic level. Often, features of interest are highly localized in specific regions in the material, such as ferroelectric domain walls, extended defects, or second phase inclusions. Selecting regions to image for structural and chemical discovery via atomically resolved imaging has traditionally proceeded via human operators making semi-informed judgements on sampling locations and parameters. Recent efforts at automation for structural and physical discovery have pointed towards the use of ‘active learning’ methods that utilize Bayesian optimization with surrogate models to quickly find relevant regions of interest. Yet despite the potential importance of this direction, there is a general lack of certainty in selecting relevant control algorithms and how to balance a priori knowledge of the material system with knowledge derived during experimentation. Here we address this gap by developing the automated experiment workflows with several combinations to both illustrate the effects of these choices and demonstrate the tradeoffs associated with each in terms of accuracy, robustness, and susceptibility to hyperparameters for structural discovery. We discuss possible methods to build descriptors using the raw image data and deep learning based semantic segmentation, as well as the implementation of variational autoencoder based representation. Furthermore, each workflow is applied to a range of feature sizes including NiO pillars within a La:SrMnO 3 matrix, ferroelectric domains in BiFeO 3 , and topological defects in graphene. The code developed in this manuscript is open sourced and will be released at github.com/nccreang/AE_Workflows .

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Search for dark matter produced in association with a Higgs boson decaying to tau leptons at $\sqrt{s}$ = 13 TeV with the ATLAS detector

A search for dark matter produced in association with a Higgs boson in final states with two hadronically decaying τ-leptons and missing transverse momentum is presented. The analysis uses 139 fb -1 of proton-proton collision data at $\sqrt{s}$ = 13 TeV collected by the ATLAS experiment at the Large Hadron Collider between 2015 and 2018. No evidence of physics beyond the Standard Model is found. The results are interpreted in terms of a 2HDM+a model featuring two scalar Higgs doublets and a pseudoscalar singlet field. Exclusion limits on the parameters of the model in selected benchmark scenarios are derived at 95% confidence level. Model-independent limits are also set on the visible cross-section for processes beyond the Standard Model producing missing transverse momentum in association with a Higgs boson decaying into τ-leptons.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Robust implementation of generative modeling with parametrized quantum circuits

Although the performance of hybrid quantum-classical algorithms is highly dependent on the selection of the classical optimizer and the circuit ansätze, a robust and thorough assessment on-hardware of such features has been missing to date. From the optimizer perspective, the primary challenge lies in the solver’s stochastic nature, and their significant variance over the random initialization. Therefore, a robust comparison requires one to perform several training curves for each solver before one can reach conclusions about their typical performance. Since each of the training curves requires the execution of thousands of quantum circuits in the quantum computer, such a robust study remained a steep challenge for most hybrid platforms available today. In this work, we leverage on Rigetti’s Quantum Cloud Services (QCS™) to overcome this implementation barrier, and we study the on-hardware performance of the data-driven quantum circuit learning (DDQCL) for three different state-of-the-art classical solvers, and on two-different circuit ansätze associated to different entangling connectivity graphs for the same task. Additionally, we assess the gains in performance from varying circuit depths. To evaluate the typical performance associated with each of these settings in this benchmark study, we use at least five independent runs of DDQCL towards the generation of quantum generative models capable of capturing the patterns of the canonical Bars and Stripes dataset. In this experimental benchmarking, the gradient-free optimization algorithms show an outstanding performance compared to the gradient-based solver. In particular, one of them had better performance when handling the unavoidable noisy objective function to be minimized under experimental conditions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Polymeric membranes for CO 2 separation and capture

Over the past decade, CO 2 separation and capture have become the new bandwagon for polymer science and membrane research. This review presents the fundamentals of CO 2 /gas separation in polymeric membranes and discusses how these principles underpin opportunities and challenges for post-combustion carbon capture (CO 2 /N 2 ), hydrogen purification (CO 2 /H 2 ), and natural gas and biogas sweetening (CO 2 /CH 4 ). Emerging polymeric membrane materials are discussed, including a few polymers containing a high content of polar functional groups (i.e., ether oxygen-rich polymers and polymeric ionic liquids), shape-persisting glassy polymers (i.e., perfluoropolymers, thermally rearranged polymers, iptycene-containing polymers), and reactive polymers featuring facilitated transport. Moreover, the promising candidates for each CO 2 separation application are highlighted. Lastly, the permeability-selectivity data reviewed were plotted against their 2008 and 2019 upper bounds.

08 HYDROGEN↗
DOE OSTI2026

Synergistic Solvent-Surface Interactions Enable Alkyne Semihydrogenation at Palladium

Enabling higher yield and better selectivity for fine-chemical synthesis through heterogeneous catalysis is intricately linked to the interplay of active sites, reaction conditions, and mass transfer influence provided by the catalyst. Alkyne semihydrogenation is ubiquitous in the production of bulk chemicals in the pharmaceutical, polymer, or fine-chemical industries, but product selectivity remains a major challenge. Here, in this study, we demonstrate that the design of catalysts encompassing nickel (Ni) foams as contiguous monolith supports, decorated with ultralow loading of Pd/PdO x nanoparticles on a carbonized polydopamine interface and tuned with a thin layer of Al 2 O 3 , in conjunction with an optimized reaction environment leads to highly selective alkyne semihydrogenation. The reactions demonstrate good functional group tolerance and applicability to flow reactor systems. Combined computational and experimental studies are presented to describe the synergistic effect between the solvent-surface interaction and the degree of Pd surface reduction that are necessary to promote this selectivity. The system highlights the opportunity for catalyst-solvent codesign as a benign alternative to more complex reactants featuring extrinsic poisons or less-favored dopants.

atomic layer deposition↗
DOE OSTI2025

Reversible parts-per-trillion-level detection of perfluorooctane sulfonic acid in tap water using field-effect transistor sensors

Widespread, persistent and toxic per- and polyfluoroalkyl substances (PFAS) pose a major threat to water systems and human health. Current detection methods are relatively expensive, slow and complex, underscoring the need for more accessible alternatives to meet increasingly stringent PFAS regulations. Here, in this study, we present an ultrasensitive sensing platform for perfluorooctane sulfonic acid detection in tap water with a reporting limit ( ~ 250 parts per quadrillion) lower than the US Environmental Protection Agency’s regulatory standard (4 parts per trillion), using a remote gate field-effect transistor featuring β-cyclodextrin (β-CD)-modified reduced graphene oxide as the sensing membrane. The sensor exhibits excellent selectivity against common inorganic ions, natural organic matter and select organic pollutants in tap water. The reversible and rapid response ( < 2 min) indicates the potential of remote gate field-effect transistor for continuous in-line monitoring. Mechanistic studies using quartz crystal microbalance and molecular dynamics simulations reveal key roles of analyte adsorption and charge properties in sensing performance and offer insights for designing more selective PFAS capture probes.

Wang, Yuqin [University of Chicago, IL (United Sta↗
DOE OSTI2025

Toward accelerating rare-earth metal extraction using equivariant neural networks

The separation of rare-earth metals, vital for numerous advanced technologies, is hampered by their similar chemical properties, making ligand discovery a significant challenge. Traditional experimental and quantum chemistry approaches for identifying effective ligands are often resource-intensive. We introduce a machine learning protocol based on an equivariant neural network, Allegro, for the rapid and accurate prediction of binding energies in rare-earth complexes. Key to this work is our newly curated dataset of rare-earth metal complexes—made publicly available to foster further research—systematically generated using the Architector program. This dataset distinctively features functionalized derivatives of proven rare-earth-chelating scaffolds, hydroxypyridinone (HOPO), catecholamide (CAM), and their thio-analogues, selected for their established efficacy in binding these elements. Trained on this valuable resource, our Allegro models demonstrate excellent performance, particularly when trained to directly predict DFT-level binding energies, yielding highly accurate results that closely correlate with theoretical calculations on a diverse test set. Furthermore, this strategy exhibited strong out-of-sample generalization, accurately predicting binding energies for an isomeric HOPO-derivative ligand not seen during training. By substantially reducing computational demands, this machine learning framework, alongside the provided dataset, represent powerful tools to accelerate the high-throughput screening and rational design of novel ligands for efficient rare-earth metal separation.

Gupta, Ankur K. [Lawrence Berkeley National Labora↗
DOE OSTI2022

Optical readout of singlet fission biexcitons in a heteroacene with photoluminescence detected magnetic resonance

Molecular spin systems based on photoexcited triplet pairs formed via singlet fission (SF) are attractive as carriers of quantum information because of their potentially pure and controllable spin polarization, but developing systems that offer optical routes to readout as well as initialization is challenging. Herein, we characterize the electron spin magnetic resonance change in the photoluminescence intensity for a tailored organic molecular crystal while sweeping a microwave drive up to 10 GHz in a broadband loop structure. We observe resonant transitions for both triplet and quintet spin sublevel populations showing their optical sensitivity and revealing the zero-field parameters for each. We map the evolution of these spectra in both microwave frequency and magnetic field, producing a pattern of optically detected magnetic resonance (ODMR) peaks. Fits to these data using a suitable model suggest significant spin polarization in this system with orientation selectivity. Unusual excitation intensity dependence is also observed, which inverts the sign of the ODMR signal for the triplet features, but not for the quintet. These observations demonstrate optical detection of the spin sublevel population dictated by SF and intermolecular geometry, and highlight anisotropic and multi-scale dynamics of triplet pairs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Polarized tip-enhanced Raman spectroscopy at liquid He temperature in ultrahigh vacuum using an off-axis parabolic mirror

Tip-enhanced Raman spectroscopy (TERS) combines inelastic light scattering well below the diffraction limit down to the nanometer range and scanning probe microscopy and, possibly, spectroscopy. In this way, topographic and spectroscopic as well as single- and two-particle information may simultaneously be collected. While single molecules can now be studied successfully, bulk solids are still not meaningfully accessible. It is the purpose of the work presented here to outline approaches toward this objective. We describe a home-built, liquid helium cooled, ultrahigh vacuum TERS. The setup is based on a scanning tunneling microscope and, as an innovation, an off-axis parabolic mirror having a high numerical aperture of ~0.85 and a large working distance. The system is equipped with a fast load-lock chamber, a chamber for the in situ preparation of tips, substrates, and samples, and a TERS chamber. Base pressure and temperature in the TERS chamber were ~3 × 10 –11 mbar and 15 K, respectively. Polarization dependent tip-enhanced Raman spectra of the vibration modes of carbon nanotubes were successfully acquired at cryogenic temperature. The new features described here including very low pressure and temperature and the external access to the light polarizations, thus the selection rules, may pave the way toward the investigation of bulk and surface materials.

47 OTHER INSTRUMENTATION↗