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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Ab Initio Molecular Dynamics Simulation of Divalent Metal Cation Incorporation in Calcite: Implications for Interpreting X-ray Absorption Spectroscopy Data

Calcite (CaCO 3 ) is a ubiquitous mineral with the ability to accommodate a wide range of impurities. Determination of the coordination environment and incorporation modes of impurities in calcite has historically relied primarily on the interpretation of extended X-ray absorption fine structure (EXAFS) spectroscopy. However, lack of standards combined with the large number of degrees of freedom involved in shell-by-shell fits have made the interpretation of EXAFS spectra challenging. As such, in this work, ab initio molecular dynamics (AIMD) simulations were performed to investigate the incorporation of seven divalent metal cation impurities, namely, Ba 2+ , Pb 2+ , Sr 2+ , Cd 2+ , Mn 2+ , Co 2+ , and Zn 2+ , in calcite. These cations span a wide range of sizes: 30.7 to –22.8% change in ionic radius with respect to Ca 2+ . The atomic trajectories were then used to compute EXAFS spectra for direct comparison with published experimental spectra. The simulations confirmed that all seven metal cations incorporate in calcite via substitution in the calcium site. The AIMD–EXAFS approach allowed for overcoming limitations of the shell-by-shell fitting approach, such as the difficulties in resolving weak backscatterers beyond the first coordination shell. As a result, the AIMD–EXAFS approach was able to provide a detailed and comprehensive characterization of the structural relaxation around divalent metal cation impurities in calcite.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Learning Physically Interpretable Atmospheric Models From Data With WSINDy

The multiscale and turbulent nature of Earth's atmosphere has historically rendered accurate weather modeling a hard problem. Recently, there has been an explosion of interest surrounding data-driven approaches to weather modeling, which in many cases show improved forecasting accuracy and computational efficiency when compared to traditional methods. However, many of the current data-driven approaches employ highly parameterized neural networks, often resulting in uninterpretable models and limited gains in scientific understanding. In this work, we address the interpretability problem by explicitly discovering partial differential equations governing atmospheric phenomena, identifying symbolic mathematical models with direct physical interpretations. The purpose of this paper is to demonstrate that, in particular, the weak-form sparse identification of nonlinear dynamics (WSINDy) algorithm can learn effective atmospheric models from both simulated and assimilated data. Our approach adapts the standard WSINDy algorithm to work with high-dimensional fluid data of arbitrary spatial dimension.

58 GEOSCIENCES↗
DOE OSTI2017

Critical Assessment of Metagenome Interpretation—a benchmark of metagenomics software

Methods for assembly, taxonomic profiling and binning are key to interpreting metagenome data, but a lack of consensus about benchmarking complicates performance assessment. The Critical Assessment of Metagenome Interpretation (CAMI) challenge has engaged the global developer community to benchmark their programs on highly complex and realistic data sets, generated from ~700 newly sequenced microorganisms and ~600 novel viruses and plasmids and representing common experimental setups. Assembly and genome binning programs performed well for species represented by individual genomes but were substantially affected by the presence of related strains. Taxonomic profiling and binning programs were proficient at high taxonomic ranks, with a notable performance decrease below family level. Parameter settings markedly affected performance, underscoring their importance for program reproducibility. The CAMI results highlight current challenges but also provide a roadmap for software selection to answer specific research questions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Correlator convolutional neural networks as an interpretable architecture for image-like quantum matter data

Image-like data from quantum systems promises to offer greater insight into the physics of correlated quantum matter. However, the traditional framework of condensed matter physics lacks principled approaches for analyzing such data. Machine learning models are a powerful theoretical tool for analyzing image-like data including many-body snapshots from quantum simulators. Recently, they have successfully distinguished between simulated snapshots that are indistinguishable from one and two point correlation functions. Thus far, the complexity of these models has inhibited new physical insights from such approaches. Here, we develop a set of nonlinearities for use in a neural network architecture that discovers features in the data which are directly interpretable in terms of physical observables. Applied to simulated snapshots produced by two candidate theories approximating the doped Fermi-Hubbard model, we uncover that the key distinguishing features are fourth-order spin-charge correlators. Our approach lends itself well to the construction of simple, versatile, end-to-end interpretable architectures, thus paving the way for new physical insights from machine learning studies of experimental and numerical data.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Critical Assessment of Metagenome Interpretation: the second round of challenges

Abstract Evaluating metagenomic software is key for optimizing metagenome interpretation and focus of the Initiative for the Critical Assessment of Metagenome Interpretation (CAMI). The CAMI II challenge engaged the community to assess methods on realistic and complex datasets with long- and short-read sequences, created computationally from around 1,700 new and known genomes, as well as 600 new plasmids and viruses. Here we analyze 5,002 results by 76 program versions. Substantial improvements were seen in assembly, some due to long-read data. Related strains still were challenging for assembly and genome recovery through binning, as was assembly quality for the latter. Profilers markedly matured, with taxon profilers and binners excelling at higher bacterial ranks, but underperforming for viruses and Archaea. Clinical pathogen detection results revealed a need to improve reproducibility. Runtime and memory usage analyses identified efficient programs, including top performers with other metrics. The results identify challenges and guide researchers in selecting methods for analyses.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Automated Interpretation and Extraction of Topographic Information from Time of Flight Secondary Ion Mass Spectrometry Data

Time of flight secondary ion mass spectrometry (ToF SIMS) is one of the most powerful characterization tools allowing imaging of the chemical properties of various systems and materials. It allows precise studies of the chemical composition with sub-100-nm lateral and nanometer depth spatial resolution. However, comprehensive interpretation of ToF SIMS results is challengeable, because of the data volume and its multidimensionality. Furthermore, investigation of the samples with pronounced topographical features are complicated by the spectral shift. In this work we developed approach for the comprehensive ToF SIMS data interpretation based on the data analytics and automated extraction of the sample topography based on time of flight shift. We further applied this approach to investigate correlation between biological function and chemical composition in Arabidopsis roots.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks

Machine learning has emerged as a powerful approach in materials discovery. Its major challenge is selecting features that create interpretable representations of materials, useful across multiple prediction tasks. We introduce an end-to-end machine learning model that automatically generates descriptors that capture a complex representation of a material’s structure and chemistry. This approach builds on computational topology techniques (namely, persistent homology) and word embeddings from natural language processing. It automatically encapsulates geometric and chemical information directly from the material system. We demonstrate our approach on multiple nanoporous metal–organic framework datasets by predicting methane and carbon dioxide adsorption across different conditions. Our results show considerable improvement in both accuracy and transferability across targets compared to models constructed from the commonly-used, manually-curated features, consistently achieving an average 25–30% decrease in root-mean-squared-deviation and an average increase of 40–50% in R 2 scores. A key advantage of our approach is interpretability: Our model identifies the pores that correlate best to adsorption at different pressures, which contributes to understanding atomic-level structure–property relationships for materials design.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Evaluating the factors influencing accuracy, interpretability, and reproducibility in the use of machine learning classifiers in biology to enable standardization

The complexity and variability of biological data has promoted the increased use of machine learning methods to understand processes and predict outcomes. These same features complicate reliable, reproducible, interpretable, and responsible use of such methods, resulting in questionable relevance of the derived. outcomes. Here we systematically explore challenges associated with applying machine learning to predict and understand biological processes using a well- characterized in vitro experimental system. We evaluated factors that vary while applying machine learning classifers: (1) type of biochemical signature (transcripts vs. proteins), (2) data curation methods (pre- and post-processing), and (3) choice of machine learning classifier. Using accuracy, generalizability, interpretability, and reproducibility as metrics, we found that the above factors significantly mod- ulate outcomes even within a simple model system. Our results caution against the unregulated use of machine learning methods in the biological sciences, and strongly advocate the need for data standards and validation tool-kits for such studies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Computational simulation and interpretation of the low-lying excited electronic states and electronic spectrum of thioanisole

Three singlet states, namely a closed-shell ground state and two excited states with 1 ππ* and 1 nσ* character, have been suggested to be responsible for the radiationless decay or photochemical reaction of photoexcited thioanisole. The correct interpretation of the electronic spectrum is critical for understanding the character of these low-lying excited states, but the experimental spectrum is yet to be fully interpreted. In the work reported here, we investigated the nature of those three states and a fourth singlet state of thioanisole using electronic structure calculations by multireference perturbation theory, by completely-renormalized equation-of-motion coupled cluster theory with single and double excitations and noniterative inclusion of connected triples (CR-EOM-CCSD(T)), and by linear-response time-dependent density functional theory (TDDFT). We clarified the assignment of the electronic spectrum by simulating it using a normal-mode sampling approach combined with TDDFT in the Tamm–Dancoff approximation (TDA). The understanding of the electronic states and of the accuracy of the electronic structure methods lays the foundation of our future work of constructing potential energy surfaces.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

Informed total-error-minimizing priors: Interpretable cosmological parameter constraints despite complex nuisance effects

While Bayesian inference techniques are standard in cosmological analyses, it is common to interpret resulting parameter constraints with a frequentist intuition. This intuition can fail, for example, when marginalizing high-dimensional parameter spaces onto subsets of parameters, because of what has come to be known as projection effects or prior volume effects. We present the method of informed total-error-minimizing (ITEM) priors to address this problem. An ITEM prior is a prior distribution on a set of nuisance parameters, such as those describing astrophysical or calibration systematics, intended to enforce the validity of a frequentist interpretation of the posterior constraints derived for a set of target parameters (e.g., cosmological parameters). Our method works as follows. For a set of plausible nuisance realizations, we generate target parameter posteriors using several different candidate priors for the nuisance parameters. We reject candidate priors that do not accomplish the minimum requirements of bias (of point estimates) and coverage (of confidence regions among a set of noisy realizations of the data) for the target parameters on one or more of the plausible nuisance realizations. Of the priors that survive this cut, we select the ITEM prior as the one that minimizes the total error of the marginalized posteriors of the target parameters. As a proof of concept, we applied our method to the density split statistics measured in Dark Energy Survey Year 1 data. We demonstrate that the ITEM priors substantially reduce prior volume effects that otherwise arise and that they allow for sharpened yet robust constraints on the parameters of interest.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Interpreting the behavior of a quarter-wave transmission line resonator in a magnetized plasma

The quarter wave resonator immersed in a strongly magnetized plasma displays two possible resonances occurring either below or above its resonance frequency in vacuum, f{sub o}. This fact was demonstrated in our recent articles [G. S. Gogna and S. K. Karkari, Appl. Phys. Lett. 96, 151503 (2010); S. K. Karkari, G. S. Gogna, D. Boilson, M. M. Turner, and A. Simonin, Contrib. Plasma Phys. 50(9), 903 (2010)], where the experiments were carried out over a limited range of magnetic fields at a constant electron density, n{sub e}. In this paper, we present the observation of dual resonances occurring over the frequency scan and find that n{sub e} calculated by considering the lower resonance frequency is 25%–30% smaller than that calculated using the upper resonance frequency with respect to f{sub o}. At a given magnetic field strength, the resonances tend to shift away from f{sub o} as the background density is increased. The lower resonance tends to saturate when its value approaches electron cyclotron frequency, f{sub ce}. Interpretation of these resonance conditions are revisited by examining the behavior of the resonance frequency response as a function of n{sub e}. A qualitative discussion is presented which highlights the practical application of the hairpin resonator for interpreting n{sub e} in a strongly magnetized plasma.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

A Look Inside the Black Box: Using graph-theoretical descriptors to interpret a Continuous-Filter Convolutional Neural Network (CF-CNN) trained on the global and local minimum energy structures of neutral water clusters

A Continuous Filter Convolutional Neural Network (CF-CNN) was trained to predict the potential energy of water cluster networks \ce{(H2O)_{\textit{N}}}, \textit{N}=10--30, corresponding to local minima lying within 5 kcal/mol from the putative minima taken from a newly published database containing over 5 million unique networks. The chemical sampling space of the database was characterized using chemical descriptors derived from graph theory, which led to the identification of important trends in the topology, connectivity, polygon structures associated with the various networks as a function of cluster size. The resulting graphs are available alongside the original database at \url{https://sites.uw.edu/wdbase/}. The CF-CNN trained on a subset of 500,000 networks for (\textit{N}=10, 30) yielded a mean absolute error of 0.002$\pm$0.002 kcal/mol per water molecule, giving the trained CF-CNN the highest accuracy of any neural network-based surrogate model to date. In addition, clusters of sizes not included in the training set exhibited errors of the same magnitude, indicating that the CF-CNN ptotocol is general enough to accurately predict energies of networks for both smaller and larger sizes than those used during training. The graph-theoretical descriptors were developed in order to analyze the properties of the full database and interpret the predictive power of the CF-CNN. Using topology measures, such as the Wiener index and the average shortest path length along with two similarity measures, we showed that all networks from the test set were within the range of the ones from the training set, suggesting that the training set covered the chemical space of interest quite well. Our graph analysis suggests that the mean degree and number of polygons for networks with larger errors tend to lie further from the mean than those with lower errors. The generality of the used CF-CNN was thus demonstrated, while the use of the graph-theoretical descriptors assisted in interpreting the predicted results.

Bilbrey, Jenna A.↗
DOE OSTI2023

Low-frequency shear Alfvén waves at DIII-D: Theoretical interpretation of experimental observations

The linear properties of the low-frequency shear Alfvén waves such as those associated with the beta-induced Alfvén eigenmodes (BAEs) and the low-frequency modes observed in reversed-magnetic-shear DIII-D discharges [W. Heidbrink et al., Nucl. Fusion 61, 066031 (2021)] are theoretically investigated and delineated based on the theoretical framework of the general fishbone-like dispersion relation (GFLDR). By adopting representative experimental equilibrium profiles, it is found that, even though both modes are predominantly of Alfvénic polarization, the low-frequency mode is a reactive unstable mode with weak coupling to the energetic particles, while the BAE involves a dissipative instability due to resonant excitation by the energetic ions. Thus, the low-frequency mode is more appropriately called a low-frequency Alfvén mode (LFAM). Moreover, the ascending frequency spectrum patterns of the experimentally observed BAEs and LFAMs can be theoretically reproduced by varying qmin and also be well interpreted based on the GFLDR. The present analysis illustrates the solid predictive capability of the GFLDR and its practical usefulness in enhancing the interpretative capability of both experimental and numerical simulation results.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Refined interpretation of electron temperature response to neutral beam injection at DIII-D

Accurate particle and power deposition profiles of neutral beam injection (NBI) are essential to transport studies, and that information is usually acquired through Monte Carlo simulations with a given collisional model. The deposition process of the energetic beam particles leads to the informative electron temperature (T e ) evolution trajectory, which can be captured by electron cyclotron emission (ECE) system due to its good spatial and temporal resolution. Previously, some work has been done to interpret the T e responses to the pulsed NBI as a linear heating source with Fourier-based techniques, although that approach fell short when the fast ion slowing-down time becomes significant (~100 ms). It has been observed in DIII-D that the modulated NBI pulses (10–50 Hz) reduce local core T e values ~0.1 keV through cold electron dilution in high-T e (>2 keV) plasmas alongside accumulative heating. Here, a novel approach to interpret the T e response to NBI was developed by linearizing and modeling the detailed T e evolution trajectory using coherently averaged ECE data based on the different time scales of the terms in the local power and particle balance equations. The technique does not require absolute calibrations of ECE and is independent of collisional models. The resulting beam deposition profiles show good consistency and reasonable agreement with Monte Carlo calculations based on the atomic data from the Atomic Data and Analysis Structure (ADAS). Local electron density response measured by Thomson scattering (TS) also suggests the same features when the beam pulse is large enough for that diagnostic to resolve. The remaining discrepancies are also discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Interpreting scattered neutron spectra on omega cryogenic implosions with measured backgrounds and higher-order scattering

Measurements of the areal density (⁠ρR⁠) of inertially confined implosions are critical to evaluate their performance. On OMEGA, ρR is inferred from measurements of the scattered neutron spectrum via neutron-time-of-flight (nTOF) and magnetic recoil spectrometer detectors. The nTOF measurements, in particular, have the ability to measure a wide range of neutron energies and thus scattering angles, with high precision. However, the neutrons that are backscattered into the detector must transit through the entire dense fuel assembly, and as a result are subject to rescattering effects, which have been heretofore neglected in the interpretation of OMEGA neutron spectra. At the backscatter edge, neglecting rescattering on OMEGA can lead to a ~10 to 20% reduction of the apparent areal density and therefore must be included in the analysis. The low ρR s on OMEGA also mean that backgrounds from non-target physics effects, such as scattering in the target chamber or slow scintillator decays, can significantly alter the measured signal. Here, we discuss how the Monte Carlo neutron spectrum postprocessor IRIS is used to include second-order scattering effects in the interpretation of the scattered neutron spectrum in OMEGA implosion experiments, and how dedicated implosion experiments are used to measure nTOF backgrounds in order to infer backscattered areal densities on OMEGA.

Deuterium↗
DOE OSTI2026

Interpreting AI for fusion: An application to plasma profile analysis for tearing mode stability

Artificial intelligence models have demonstrated strong predictive capabilities for various instabilities in fusion devices such as Tokamaks, including tearing modes (TM), edge localized modes, and disruptive events, but their opaque nature raises concerns about safety and trustworthiness when applied to fusion power plants. Here, we present a physics-based interpretation framework using a TM prediction model as a demonstration that is validated through a dedicated DIII-D TM avoidance experiment. By applying Shapley analysis, we identify how profiles such as rotation, temperature, and density contribute to the model's prediction of TM stability. Our analysis shows that in our experimental scenario, core electron temperature and rotation peaking play the primary role in TM stability, while density changes have smaller effects on stability. We show that off-axis ion temperature stabilizes TMs, suggesting that off-axis neutral beam heating can further stabilize this scenario. This work presents a generalizable ML-based event prediction methodology, from training to physics-driven interpretation, bridging the gap between physics understanding and opaque ML models.

Farre-Kaga, Hiro J. [Princeton Univ., NJ (United S↗
DOE OSTI2013

INTERPRETING THE ROLE OF THE MAGNETIC FIELD FROM DUST POLARIZATION MAPS

Dust polarization observations from the Submillimeter Array (SMA) and the Caltech Submillimeter Observatory (CSO) are analyzed with the goal of providing a general tool to interpret the role of the magnetic field in molecular clouds. Magnetic field and dust emission gradient orientations are observed to show distinct patterns and features. The angle δ between these two orientations can be interpreted as a magnetic field alignment deviation, assuming the emission gradient orientation to coincide with the density gradient orientation in the magnetohydrodynamics force equation. In SMA high-resolution (collapsing) cores, additional symmetry properties in δ can reveal accretion and outflow zones. All these observational findings suggest the angle δ to be a relevant quantity that can assess the role of the magnetic field. Indeed, when comparing this angle with the (projection-free) magnetic field significance Σ{sub B} (introduced by Koch and coworkers in 2012), it is demonstrated that |δ| yields an approximation to the change in Σ{sub B}. Thus, changes in the magnetic field alignment deviation δ trace changes in the role of the magnetic field. The angle δ is observationally straightforward to determine, providing a tool to distinguish between zones of minor or significant magnetic field impact. This is exemplified by the CSO M+0.25 + 0.01, Mon R2, CO+0.02 – 0.02, M–0.02 – 0.07 sources and by the SMA high-resolution data from W51 e2, W51 North, Orion BN/KL and g5.89. Additional CSO sources are analyzed, providing further support of this result. Finally, based on the different features found in our sample of 31 sources in total, covering sizes from large-scale complexes to collapsing cores, a schematic evolutionary scenario is proposed. Here, the significance of the magnetic field evolves both with position and scale, and can be assessed with the angle δ.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

Interpretation of a short-term anomaly in the gravitational microlensing event MOA-2012-BLG-486

A planetary microlensing signal is generally characterized by a short-term perturbation to the standard single lensing light curve. A subset of binary-source events can produce perturbations that mimic planetary signals, thereby introducing an ambiguity between the planetary and binary-source interpretations. In this paper, we present the analysis of the microlensing event MOA-2012-BLG-486, for which the light curve exhibits a short-lived perturbation. Routine modeling not considering data taken in different passbands yields a best-fit planetary model that is slightly preferred over the best-fit binary-source model. However, when allowed for a change in the color during the perturbation, we find that the binary-source model yields a significantly better fit and thus the degeneracy is clearly resolved. This event not only signifies the importance of considering various interpretations of short-term anomalies, but also demonstrates the importance of multi-band data for checking the possibility of false-positive planetary signals.

79 ASTRONOMY AND ASTROPHYSICS↗