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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

New interpretation of ion cyclotron emission from a tokamak

The tempting interpretation of ion cyclotron emission in terms of compressional Alfvén eigenmodes involving energetic ions is inconsistent with recent TCV experimental observations in some important aspects, such as (i) the perturbed poloidal field is exceeding the parallel perturbed magnetic field significantly, and (ii) the modes are near cyclotron harmonic and exhibit Alfvèn scaling of their frequency. We show that these characteristics can be explained by considering finite Larmor radius effects of thermal ions in shear Alfvén waves that allow such waves to exist well above the ion cyclotron frequency in the form of wave-packets bouncing within the plasma volume.

Plasma waves↗
DOE OSTI2026

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

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

Bayesian optimization↗
DOE OSTI2024

Super-X and conventional divertor configurations in MAST-U ohmic L-mode; a comparison facilitated by interpretative modelling

Measurements are presented, alongside corresponding interpretative SOLPS-ITER simulations, of the first MAST-U experiments comparing ohmically heated L-mode fuelling scans in Conventional divertor (CD) and Super-X divertor (SXD) configurations. In experiment, at comparable outer mid-plane separatrix electron density, $n_{e,\textrm{sep,OMP}}$, the maximum lower outer target heat load was found to be a factor 16 $\,\pm\,7$ lower in SXD compared to CD. In simulation, a factor 26.8 reduction was found (slightly higher than the experimental range), suggesting an additional reduction in SXD compared to the factor 9.3 expected from geometric considerations alone. According to the simulations, this additional reduction in the SXD is due to a net radial transport of the energy remaining downstream of the $T_e = 5$ eV location. This energy is carried out of the critical (highest heat load) flux tube by deuterium atoms, demonstrating the importance of a longer legged divertor which provides space for this to occur. Importantly, in both simulation and experiment, the SXD has minimal impact on the upstream n e and T e profiles. Spectral inferences of detachment front movement in SXD compare well between simulation and experiment. In regions of high magnetic field gradient, the parallel movement of the front towards the X-point becomes less sensitive to increasing $n_{e,\textrm{sep,OMP}}$, in qualitative agreement with simplified models and previous predictive simulations. Additional aspects, regarding the target ion flux rollover, upstream separatrix temperature and drift effects, are also presented and discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Interpretive modeling of tungsten divertor leakage during experiments with neon gas seeding

Abstract Many existing and future tokamaks with tungsten divertors operate, or will operate, with low- Z impurity seeding, but the direct effect of these seeded impurities on tungsten Scrape-off-Layer (SOL) transport has not been explored in detail. This paper reports on a DIII-D experiment designed to test how tungsten divertor leakage from the Small-Angle Slot V-Shaped, tungsten-coated divertor is impacted by neon seeding at a variety of injection rates and poloidal injection locations. Measurements from the experiment show an inverse relationship between the neon injection rate and the tungsten core penetration factor. Interpretive modeling is performed with a combination of the SOLPS-ITER and DIVIMP codes to assess the underlying tungsten behavior. The modeling results show that the reduction in tungsten divertor leakage is driven by both an increase in the divertor collisionality as well as a reduction in the ion temperature gradient near the divertor target. Collisions between low- Z impurities and tungsten impurities are found to have a significant impact on the tungsten SOL transport, such that ignoring the low- Z impurity collisional effects on the tungsten transport can result in an overestimate of the divertor leakage by an order-of-magnitude. Given the importance of these localized interactions, neon seeding from the closed, slot-like divertor has a clear advantage in being able to reduce tungsten divertor leakage without the high levels of neon core contamination that occur when seeding from other poloidal locations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Enzyme Engineering Database (EnzEngDB): a platform for sharing and interpreting sequence–function relationships across protein engineering campaigns

The discovery and engineering of new enzymes is important across the bioeconomy, with diverse applications from foods to pharmaceuticals, sensors to agriculture. However, enzyme engineering, in particular machine learning-guided engineering, is hampered by a lack of data. Currently there exists no database designed to capture and interpret datasets created in this domain, nor are there easy analysis and visualisation tools. We developed the Enzyme Engineering Database to provide a centralized resource and an online analysis tool to consolidate sequence-function data from enzyme engineering campaigns, thereby making three contributions: (i) a database into which researchers can deposit public data, (ii) visualisation and analysis tools for protein engineers to analyse their own data or compare enzyme variants to other engineering campaigns, and (iii) a gold-standard dataset for benchmarking automated extraction along with the first large language model extraction pipeline specific for enzyme engineering campaigns. The Enzyme Engineering Database is accessible at http://enzengdb.org/.

Long, Yueming [California Institute of Technology ↗
DOE OSTI2025

Learning PDFs through interpretable latent representations in Mellin space

Representing the parton distribution functions (PDFs) of the proton and other hadrons through flexible, high-fidelity parametrizations has been a long-standing goal of particle physics phenomenology. This is particularly true since the chosen parametrization methodology can play an influential role in the ultimate PDF uncertainties as extracted in QCD global analyses; these, in turn, are often determinative of the reach of experiments at the LHC and other facilities to nonstandard physics, including at large 𝑥, where parametrization effects can be significant. In this study, we explore a series of encoder-decoder machine-learning (ML) models with various neural-network topologies as efficient means of reconstructing PDFs from meaningful information stored in an interpretable latent space. Given recent effort to pioneer synergies between QCD analyses and lattice-gauge calculations, we formulate a latent representation based on the behavior of PDFs in Mellin space, i.e., their integrated moments, and test the ability of various models to decode PDFs from this information faithfully. We introduce a numerical package, PDFdecoder, which implements several encoder-decoder models to reconstruct PDFs with high fidelity and use this end-to-end tool to explore how such neural-network-based models might connect PDF parametrizations to underlying properties like their Mellin moments. We additionally dissect patterns of learned correlations between encoded Mellin moments and reconstructed PDFs that suggest opportunities for further improvements to ML-based approaches to PDF parametrizations and uncertainty quantification.

Machine learning↗
DOE OSTI2025

Interpretable machine learning-guided design of Fe-based soft magnetic alloys

Here, we present a machine learning (ML) guided approach to predict saturation magnetization (𝑀 S ) and coercivity (𝐻 C ) in Fe-rich soft magnetic alloys, particularly Fe-Si-B systems. ML models trained on experimental data reveal that increasing Si and B content reduces 𝑀 S from 1.81 T (DFT ≈ 2.04 T) to ≈1.54 T (DFT ≈ 1.56T) in Fe-Si-B, which is attributed to decreased magnetic density and structural modifications. Experimental validation of ML predicted magnetic saturation on Fe-1Si-1B (2.09 T), Fe-5Si-5B (2.01 T), and Fe-10Si-10B (1.54 T) alloy compositions further supports our findings. These trends are consistent with density functional theory predictions, which link increased electronic disorder and band broadening to lower 𝑀 S values. Experimental validation on selected alloys confirms the predictive accuracy of the ML model, with good agreement across compositions. Beyond predictive accuracy, detailed uncertainty quantification and model interpretability including through feature importance and partial dependence analysis reveal that 𝑀 S is governed by a nonlinear interplay between Fe content and early transition metal ratios, while 𝐻 C is more sensitive to processing conditions such as ribbon thickness and thermal treatment windows. The ML framework was further applied to Fe-Si-B/Cr/Cu/Zr/Nb alloys in a pseudoquaternary compositional space, which shows comparable magnetic properties to NANOMET (Fe 84.8 ⁢Si 0.5 ⁢B 9.4 ⁢Cu 0.8⁢ P 3.5 ⁢C 1 ), FINEMET (Fe 73.5 ⁢Si 13.5 ⁢B 9 Cu 1 ⁢Nb 3 ), NANOPERM (Fe 88 ⁢Zr 7⁢ B 4 ⁢Cu 1 ), and HITPERM (Fe 44 ⁢Co 44 ⁢Zr 7⁢ B 4 ⁢Cu 1 . Our findings demonstrate the potential of the ML framework for accelerated search of high-performance soft magnetic materials.

density functional theory↗
DOE OSTI2025

Arsenic Accumulation in Microbial Biomass and the Interpretation of Signals of Early Arsenic‐Based Metabolisms

Carbonaceous particles that concentrate arsenic in microbialites as old as ~3.5 Ga are similar to As-rich organic globules in modern microbialites. The former particles have been interpreted as tracers of As cycling by early microbial metabolisms. However, it is unclear if arsenic accumulation is a consequence of biological activity or passive postmortem binding of arsenic by organic matter during diagenesis in volcanically influenced, As-rich environments. Here, we address this uncertainty by evaluating the concentrations, speciation, and detectability of As in active or heat-killed biofilms formed by cyanobacteria or anoxygenic photosynthetic microbes exposed to environmentally relevant concentrations of As(III) or As(V) (50 μM to 3 mM). The genomes or metagenomes of these biofilms contain genes involved in detoxifying or energy-yielding As metabolisms. Biomass accumulates As from the solution in a concentration-dependent manner and with a preference for oxidized As(V) over As(III). Autoclaved biomass accumulates As even more strongly than active biomass, likely because living biofilms actively detoxify As. Active biofilms oxidize and reduce As and accumulate both As(III) and As(V), whereas a small fraction of As(V) can be reduced in inactive biofilms that bind As during diagenesis. Arsenic enrichments in the biomass are detectable by X-ray based spectroscopy techniques (XRF, EPMA-WDS) that are commonly used to analyze geological materials. These findings enable the reconstruction of past active and passive interactions of microbial biomass with arsenic in fossilized microbial biofilms and microbialites from the early Earth.

Madrigal‐Trejo, David↗
DOE OSTI2024

An interpretable model of pre-mRNA splicing for animal and plant genes

Pre-mRNA splicing is a fundamental step in gene expression, conserved across eukaryotes, in which the spliceosome recognizes motifs at the 3' and 5' splice sites (SSs), excises introns, and ligates exons. SS recognition and pairing is often influenced by protein splicing factors (SFs) that bind to splicing regulatory elements (SREs). Here, we describe SMsplice, a fully interpretable model of pre-mRNA splicing that combines models of core SS motifs, SREs, and exonic and intronic length preferences. We learn models that predict SS locations with 83 to 86% accuracy in fish, insects, and plants and about 70% in mammals. Learned SRE motifs include both known SF binding motifs and unfamiliar motifs, and both motif classes are supported by genetic analyses. Our comparisons across species highlight similarities between non-mammals, increased reliance on intronic SREs in plant splicing, and a greater reliance on SREs in mammalian splicing.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Topological Interpretability for Deep Learning

With the growing adoption of AI-based systems across everyday life, the need to understand their decision-making mechanisms is correspondingly increasing. The level at which we can trust the statistical inferences made from AI-based decision systems is an increasing concern, especially in high-risk systems such as criminal justice or medical diagnosis, where incorrect inferences may have tragic consequences. Despite their successes in providing solutions to problems involving real-world data, deep learning (DL) models cannot quantify the certainty of their predictions. These models are frequently quite confident, even when their solutions are incorrect. This work presents a method to infer prominent features in two DL classification models trained on clinical and non-clinical text by employing techniques from topological and geometric data analysis. We create a graph of a model's feature space and cluster the inputs into the graph's vertices by the similarity of features and prediction statistics. We then extract subgraphs demonstrating high-predictive accuracy for a given label. These subgraphs contain a wealth of information about features that the DL model has recognized as relevant to its decisions. We infer these features for a given label using a distance metric between probability measures, and demonstrate the stability of our method compared to the LIME and SHAP interpretability methods. This work establishes that we may gain insights into the decision mechanism of a DL model. This method allows us to ascertain if the model is making its decisions based on information germane to the problem or identifies extraneous patterns within the data.

Spannaus, Adam↗
DOE OSTI2024

Extracting and Interpreting Electrochemical Impedance Spectra (EIS) from Physics-Based Models of Lithium-Ion Batteries

This paper implements a highly efficient algorithm to extract electrochemical impedance spectra (EIS) from physics-based battery models (e.g., a P2D model). The mathematical approach is different from how EIS is practiced experimentally. Experimentally, the voltage (current) is harmonically perturbed over a wide range of frequencies and the amplitude and phase shift of the corresponding current (voltage) is measured. The experimental approach can be implemented in simulation software, but is computationally expensive. The approach here is to determine locally linear state-space models from the full physical model. The four Jacobian matrices that are the basis of the state-space models can be derived by numerical differentiation of the physical model. The EIS is then extracted from the state-space model using computationally efficient matrix-manipulation techniques. The algorithm can evaluate the full EIS at an instant in time during a transient, independent of whether the battery is in a stationary state. The approach is also able to separate the full-cell impedance to evaluate partial EIS, such as for a battery anode alone. Although such partial EIS is difficult to measure experimentally, the partial EIS provides valuable insights in interpreting the full-cell EIS.

25 ENERGY STORAGE↗
DOE OSTI2025

Guidelines for Making, Interpreting, and Quantifying Activity Measurements of Molten Salts Containing Mixed Cations and Anions Using Cation-Based Electrodes by Electromotive Force

Measurements of thermochemical properties in molten salt electrolytes with mixtures of cations and anions are difficult to interpret. Electromotive force measurements of salts containing common anions were driven by the least stable anion compound (e.g., LiCl in LiCl-KCl), while measurements in common cation salts were driven by the most noble anion half-cell reaction (e.g., F − /F 2 in LiCl-LiF). Measurements in mixed cation and anion salts were driven by the least stable anion compound for the most noble anion half-cell reaction (e.g., KF in KCl-LiF). These guidelines enable quantification of the activity of electroactive species in salts containing multiple cations or anions.

Lichtenstein, Timothy [Argonne National Laboratory↗
DOE OSTI2026

AIF for Vis (Active Inference for simulating human interpretation of data visualization) [SWR-26-084]

AIF for Vis contains the Active Inference models and analysis scripts used to study a simple visualization-interpretation task: estimating the average value of two bars in a bar chart. The work is a proof of concept for translating hypothesized cognitive strategies into executable, inspectable process models. We implement two idealized strategies inspired by dual-process accounts of visualization-aided decision making: *Fast model: a compressed, heuristic strategy that estimates the visual midpoint of the two bars and maintains a single belief over their average. *Slow model: a sequential, analytic strategy that estimates the two bar heights separately and maintains them in working memory before computing an average. Both models use a common Active-Inference-inspired framework for sequential perception, belief updating, action selection, and reporting. Their different internal representations produce distinct predicted vulnerabilities: *the Fast model is more susceptible to tick-salience bias; *the Slow model is more susceptible to working-memory decay. The repository includes the model implementations, scripts used for the experiments reported in the paper, precomputed trial-level results, and plotting scripts.

Goldwyn, Harrison [National Laboratory of the Rock↗
DOE OSTI2024

Utah FORGE 8-3637: Integrated Diagnostics for Interpreting Doublet Heat Sweep Efficiency - 2024 Annual Workshop Presentation

This is a presentation on the Integrated Diagnostics for Interpreting Doublet Heat Sweep Efficiency by Texas Tech University, presented by Smith Leggett. This video slide presentation discusses the ID squared technical objectives to develop and integrate diagnostic tools to determine (1) the number of fractures, (2) the uniformity of flow distribution, (3) heat exchange areas, and (4) heat efficiency. This presentation was featured in the Utah FORGE R&D Annual Workshop on August 15, 2024.

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

Data from: "Towards CONUS-Wide ML-Augmented Conceptually-Interpretable Modeling of Catchment-Scale Precipitation-Storage-Runoff Dynamics"

This data package was generated to support the manuscript “Towards CONUS-Wide Machine Learning-Augmented Conceptually Interpretable Modeling of Catchment-Scale Precipitation-Storage-Runoff Dynamics.” It provides input files, model outputs, plotting data, scripts, notebooks, and documentation used to develop, evaluate, and reproduce Mass-Conserving Perceptron (MCP)-based hydrologic modeling experiments across 513 selected Catchment Attributes and Meteorology for Large-sample Studies in the United States (CAMELS-US) basins. The files are organized by modeling component and analysis purpose, including rainfall–runoff experiments, snow module experiments, coupled hydrologic-snow experiments, Long Short-Term Memory (LSTM) benchmark results, model skill metrics, initialization and epoch records, cell-state normalization files, Akaike Information Criterion (AIC)-based model comparison files, and data used to generate manuscript figures. Tabular files can be opened using standard spreadsheet software or Python/R data-analysis tools. Python scripts, Jupyter notebooks, and selected MATLAB scripts are included for model execution, postprocessing, plotting, and statistical analysis. Quality assurance and quality control were conducted through the source-data selection and modeling workflow. Meteorological forcing, streamflow, and static catchment attributes were derived from the CAMELS-US dataset, and snow water equivalent data were derived from the University of Arizona (UA) Snow Water Equivalent dataset. Selected basins and time periods were screened during the associated research workflow to avoid missing observations or poor-quality cases. Static geospatial features were processed primarily using Quantum Geographic Information System (QGIS) and Geospatial Data Abstraction Library (GDAL) workflows. Additional details are provided in the associated manuscript and documentation.

ESS-DIVE CSV File Formatting Guidelines Reporting ↗
DOE OSTI2024

Integrating adaptive learning with post hoc model explanation and symbolic regression to build interpretable surrogate models

Abstract We develop a materials informatics workflow to build an interpretable surrogate model for micromagnetic simulations. Our goal is to predict the energy barrier of a moving isolated skyrmion in rare-earth-free $$\hbox {Mn}_4$$ Mn 4 N. Our approach integrates adaptive learning with post hoc model explanation and symbolic regression methods. We discuss an unexplored acquisition function (information condensing active learning) within the adaptive learning loop and compare it with the known standard deviation function for efficient navigation of the search space. Model-agnostic post hoc explanation techniques then uncover trends learned by the trained model, which we then leverage to constrain the expressions used for symbolic regression. Graphical abstract

Biswas, Ankita↗
DOE OSTI2024

Measures of Holographic Correlation: Discovery, Interpretation, Application (Final Report)

The main goals of this project were twofold: (1) to find measures of quantum correlations that are amenable to interpretation in a holographic context, and use these measures to develop a more detailed understanding of holography itself and (2) To use insights developed in a holographic setting to better understand the theory of quantum information in more traditional (nonholographic) settings.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Interpreting and Accelerating Transformers for Jet Tagging

Attention-based transformers are ubiquitous in machine learning applications from natural language processing to computer vision. In high energy physics, one central application is to classify collimated particle showers in colliders based on the particle of origin, known as jet tagging. In this work, we study the interpretatbility and prospects for acceleration of Particle Transformer (ParT), a state-of-the-art model, leverages particle-level attention to improve jet-tagging performance. We analyzing ParT's attention maps and particle-pair correlations in the eta-phi plane, revealing intriguing features, such as a binary attention pattern that identifies critical substructure in jets. These insights enhance our understanding of the model's internal workings and learning process and hint at ways to improve its efficiency. Along these lines, we also explore low-rank attention, attention alternatives, and dynamic quantization to accelerate transformers for jet tagging. With quantization, we achieve a 50% reduction in model size and a 10% increase in inference speed without compromising accuracy. These combined efforts enhance both the performance and the interpretability of transformers in high-energy physics, opening avenues for more efficient and physics-driven model designs.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗