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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A robust and interpretable machine learning approach using multimodal biological data to predict future pathological tau accumulation

The early stages of Alzheimer’s disease (AD) involve interactions between multiple pathophysiological processes. Although these processes are well studied, we still lack robust tools to predict individualised trajectories of disease progression. Here, we employ a robust and interpretable machine learning approach to combine multimodal biological data and predict future pathological tau accumulation. In particular, we use machine learning to quantify interactions between key pathological markers (β-amyloid, medial temporal lobe atrophy, tau and APOE 4) at mildly impaired and asymptomatic stages of AD. Using baseline non-tau markers we derive a prognostic index that: (a) stratifies patients based on future pathological tau accumulation, (b) predicts individualised regional future rate of tau accumulation, and (c) translates predictions from deep phenotyping patient cohorts to cognitively normal individuals. Our results propose a robust approach for fine scale stratification and prognostication with translation impact for clinical trial design targeting the earliest stages of AD.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Sterol methyltransferases in uncultured bacteria complicate eukaryotic biomarker interpretations

Abstract Sterane molecular fossils are broadly interpreted as eukaryotic biomarkers, although diverse bacteria also produce sterols. Steranes with side-chain methylations can act as more specific biomarkers if their sterol precursors are limited to particular eukaryotes and are absent in bacteria. One such sterane, 24-isopropylcholestane, has been attributed to demosponges and potentially represents the earliest evidence for animals on Earth, but enzymes that methylate sterols to give the 24-isopropyl side-chain remain undiscovered. Here, we show that sterol methyltransferases from both sponges and yet-uncultured bacteria function in vitro and identify three methyltransferases from symbiotic bacteria each capable of sequential methylations resulting in the 24-isopropyl sterol side-chain. We demonstrate that bacteria have the genomic capacity to synthesize side-chain alkylated sterols, and that bacterial symbionts may contribute to 24-isopropyl sterol biosynthesis in demosponges. Together, our results suggest bacteria should not be dismissed as potential contributing sources of side-chain alkylated sterane biomarkers in the rock record.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Quantifying disorder one atom at a time using an interpretable graph neural network paradigm

Abstract Quantifying the level of atomic disorder within materials is critical to understanding how evolving local structural environments dictate performance and durability. Here, we leverage graph neural networks to define a physically interpretable metric for local disorder, called SODAS. This metric encodes the diversity of the local atomic configurations as a continuous spectrum between the solid and liquid phases, quantified against a distribution of thermal perturbations. We apply this methodology to four prototypical examples with varying levels of disorder: (1) grain boundaries, (2) solid-liquid interfaces, (3) polycrystalline microstructures, and (4) tensile failure/fracture. We also compare SODAS to several commonly used methods. Using elemental aluminum as a case study, we show how our paradigm can track the spatio-temporal evolution of interfaces, incorporating a mathematically defined description of the spatial boundary between order and disorder. We further show how to extract physics-preserved gradients from our continuous disorder fields, which may be used to understand and predict materials performance and failure. Overall, our framework provides a simple and generalizable pathway to quantify the relationship between complex local atomic structure and coarse-grained materials phenomena.

36 MATERIALS SCIENCE↗
DOE OSTI2020

On-the-fly active learning of interpretable Bayesian force fields for atomistic rare events

Abstract Machine learned force fields typically require manual construction of training sets consisting of thousands of first principles calculations, which can result in low training efficiency and unpredictable errors when applied to structures not represented in the training set of the model. This severely limits the practical application of these models in systems with dynamics governed by important rare events, such as chemical reactions and diffusion. We present an adaptive Bayesian inference method for automating the training of interpretable, low-dimensional, and multi-element interatomic force fields using structures drawn on the fly from molecular dynamics simulations. Within an active learning framework, the internal uncertainty of a Gaussian process regression model is used to decide whether to accept the model prediction or to perform a first principles calculation to augment the training set of the model. The method is applied to a range of single- and multi-element systems and shown to achieve a favorable balance of accuracy and computational efficiency, while requiring a minimal amount of ab initio training data. We provide a fully open-source implementation of our method, as well as a procedure to map trained models to computationally efficient tabulated force fields.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Efficient and interpretable graph network representation for angle-dependent properties applied to optical spectroscopy

Abstract Graph neural networks are attractive for learning properties of atomic structures thanks to their intuitive graph encoding of atoms and bonds. However, conventional encoding does not include angular information, which is critical for describing atomic arrangements in disordered systems. In this work, we extend the recently proposed ALIGNN (Atomistic Line Graph Neural Network) encoding, which incorporates bond angles, to also include dihedral angles (ALIGNN-d). This simple extension leads to a memory-efficient graph representation that captures the complete geometry of atomic structures. ALIGNN-d is applied to predict the infrared optical response of dynamically disordered Cu(II) aqua complexes, leveraging the intrinsic interpretability to elucidate the relative contributions of individual structural components. Bond and dihedral angles are found to be critical contributors to the fine structure of the absorption response, with distortions that represent transitions between more common geometries exhibiting the strongest absorption intensity. Future directions for further development of ALIGNN-d are discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Ultra-fast interpretable machine-learning potentials

Abstract All-atom dynamics simulations are an indispensable quantitative tool in physics, chemistry, and materials science, but large systems and long simulation times remain challenging due to the trade-off between computational efficiency and predictive accuracy. To address this challenge, we combine effective two- and three-body potentials in a cubic B-spline basis with regularized linear regression to obtain machine-learning potentials that are physically interpretable, sufficiently accurate for applications, as fast as the fastest traditional empirical potentials, and two to four orders of magnitude faster than state-of-the-art machine-learning potentials. For data from empirical potentials, we demonstrate the exact retrieval of the potential. For data from density functional theory, the predicted energies, forces, and derived properties, including phonon spectra, elastic constants, and melting points, closely match those of the reference method. The introduced potentials might contribute towards accurate all-atom dynamics simulations of large atomistic systems over long-time scales.

36 MATERIALS SCIENCE↗
DOE OSTI2020

DDxNet: a deep learning model for automatic interpretation of electronic health records, electrocardiograms and electroencephalograms

Effective patient care mandates rapid, yet accurate, diagnosis. With the abundance of non-invasive diagnostic measurements and electronic health records (EHR), manual interpretation for differential diagnosis has become time-consuming and challenging. This has led to wide-spread adoption of AI-powered tools, in pursuit of improving accuracy and efficiency of this process. While the unique challenges presented by each modality and clinical task demand customized tools, the cumbersome process of making problem-specific choices has triggered the critical need for a generic solution to enable rapid development of models in practice. In this spirit, we develop DDxNet, a deep architecture for time-varying clinical data, which we demonstrate to be well-suited for diagnostic tasks involving different modalities (ECG/EEG/EHR), required level of characterization (abnormality detection/phenotyping) and data fidelity (single-lead ECG/22-channel EEG). Using multiple benchmark problems, we show that DDxNet produces high-fidelity predictive models, and sometimes even provides significant performance gains over problem-specific solutions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Interpretable machine learning models classify minerals via spectroscopy

Developing methods to identify mineral species confidently and rapidly from Raman spectral analysis is critical to numerous fields. Traditionally, analysis relies on pattern matching the Raman spectrum of an unknown dataset with a supporting library of well-characterized spectral data, which may prove difficult for environmental samples that are poorly crystalline or phase mixtures. Here, we developed interpretable machine learning models that can classify uranium minerals by secondary oxyanion chemistry and other physicochemical properties based solely on Raman spectra. This new ML method produces a mineral profile of physical and chemical properties for an unknown sample and can rapidly classify or identify unknown minerals from Raman data, without the need for an exact pattern match in a spectral library. Training models are validated by 1. Strong correlation of high confidence model regions with published spectroscopic assignments and 2. Correct classification of a mineral not present in training data. Training data are from the Compendium of Uranium Raman and Infrared Experimental Spectra and available crystallographic information files within the open-source Smart Spectral Matching scientific framework. Physically meaningful classifier models can rapidly identify key structural and chemical information about unknown uranium minerals and the overall methodology is broadly applicable for mineral phases.

Machine learning↗
DOE OSTI2025

Interpretable ensemble learning unveils main aerosol optical properties in predicting cloud condensation nuclei number concentration

Variations in cloud condensation nuclei number concentration (N CCN ) significantly influence cloud microphysics, yet direct N CCN measurements remain challenging. Here, we present an N CCN ensemble learning (NEL) model utilizing ensemble learning and interpretability analysis on aerosol optical parameters. Validated at two land sites, two ocean sites and one polar site within the Atmospheric Radiation Measurement program, the mean absolute percentage error range of the NEL model across different environments is from 12% to 36%, demonstrating high accuracy. Key findings reveal that aerosol optical parameters can serve as predictors for N CCN . Aerosol scattering and backscattering coefficients, absorption coefficient, backscatter fraction (BSF), and Ångström exponent (AE) are positively correlated with N CCN , while single scattering albedo shows negative correlations. N CCN prediction at land sites is highly sensitive to BSF, largely driven by the backscattering coefficient, as fine particles dominate in these sites. At ocean sites, N CCN prediction is more sensitive to AE, primarily influenced by the scattering coefficient, due to the higher proportion of larger particles. At the polar site, N CCN prediction shows sensitivity to both BSF and AE, mainly driven by the scattering coefficient, as polar sites are cleaner and contain larger particles. These differences reflect the variation in particle size and number concentration across different environments.

Atmospheric science↗
DOE OSTI2020

Interpreting Tafel behavior of consecutive electrochemical reactions through combined thermodynamic and steady state microkinetic approaches

Assessing the reaction pathway of multi-electron-transfer reactions is an essential yet difficult task for the rational design of electrocatalysts. In this work, we develop a heuristic approach that combines thermodynamic adsorption energetics calculated through density functional theory with microkinetic modeling using the steady state approximation to interpret the potential-dependent Tafel behavior of consecutive electrochemical reactions. In doing so, we introduce a kinetic framework for ab initio calculations that ensures self-consistent adsorption energetics based on kinetically limited adsorbate coverages. The approach is applied to experimental results on CoO x (OH) 2-x single crystal electrocatalyst particles yielding coverage dependent mechanistic information and identification of the rate-limiting step with standard rate constants for the oxygen evolution reaction on the ($11\bar{2}0$) surfaces of the β-Co(OH) 2 , β-CoOOH, and CoO 2 bulk phases. Finally, this generalizable method enables catalyst benchmarking based on determining the active species involved and associated intrinsic reaction rate constants in consecutive multi-electron-transfer reactions.

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

The persistent shadow of the supermassive black hole of M87. II. Model comparisons and theoretical interpretations

The Event Horizon Telescope (EHT) observation of M87∗ in 2018 has revealed a ring with a diameter that is consistent with the 2017 observation. The brightest part of the ring is shifted to the southwest from the southeast. In this paper, we provide theoretical interpretations for the multi-epoch EHT observations for M87∗ by comparing a new general relativistic magnetohydrodynamics model image library with the EHT observations for M87∗ in both 2017 and 2018. The model images include aligned and tilted accretion with parameterized thermal and nonthermal synchrotron emission properties. The 2018 observation again shows that the spin vector of the M87∗ supermassive black hole is pointed away from Earth. A shift of the brightest part of the ring during the multi-epoch observations can naturally be explained by the turbulent nature of black hole accretion, which is supported by the fact that the more turbulent retrograde models can explain the multi-epoch observations better than the prograde models. The EHT data are inconsistent with the tilted models in our model image library. Assuming that the black hole spin axis and its large-scale jet direction are roughly aligned, we expect the brightest part of the ring to be most commonly observed 90 deg clockwise from the forward jet. This prediction can be statistically tested through future observations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Hydra: An AI-Based Framework for Interpretable and Portable Data Quality Monitoring

Hydra is an advanced framework designed for training and managing AI models for near real time data quality monitoring at Jefferson Lab. Deployed in all four experimental halls, Hydra has analyzed over 2 million images and has extended its capabilities to offline monitoring and validation. Hydra utilizes computer vision to continually analyze sets of images of monitoring plots generated 24/7 during experiments. Generally, these sets of images are produced at a rate and quantity that is exceedingly difficult for shift crews to effectively monitor. Significant effort has been devoted to enhancing Hydra’s user interface, to ensure that it provides clear, actionable insights for shift workers and other users. Gradient Weighted Class Activation Maps (GradCAM) provide added interpretability, allowing users to visualize important regions of the image for classification. Hydra has been containerized to enable the creation of portable demos and seamless integration with container-based technologies such as Kubernetes and Docker. With the user interface enhancements and containerization, Hydra can be rapidly deployed for new use cases and experiments. This talk will describe the Hydra framework, its user interface and experience, and the challenges inherent in its design and deployment.

Britton, Thomas [Thomas Jefferson National Acceler↗
DOE OSTI2013

Communication: The correct interpretation of surface hopping trajectories: How to calculate electronic properties

In a recent paper, we presented a road map for how Tully's fewest switches surface hopping (FSSH) algorithm can be derived, under certain circumstances, from the mixed quantum-classical Liouville equation. In this communication, we now demonstrate how this new interpretation of surface hopping can yield significantly enhanced results for electronic properties in nonadiabatic calculations. Specifically, we calculate diabatic populations for the spin-boson problem using FSSH trajectories. We show that, for some Hamiltonians, without changing the FSSH algorithm at all but rather simply reinterpreting the ensemble of surface hopping trajectories, we recover excellent results and remove any and all ambiguity about the initial condition problem.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

Comparison of various interpretation methods of the electric probe measurements in inductively coupled Ar and O{sub 2} plasmas

In low-pressure inductively coupled argon and oxygen discharges, the plasma density and electron temperature and the electron energy distribution function (EEDF) were obtained by using a cylindrical electric probe. The plasma densities were determined by various methods to interpret the probe current-voltage characteristic curve: the EEDF integration, the electron saturation current, the ion current at the floating potential, and the orbital-motion-limited (OML) ion current. Quite a good agreement exists between the plasma densities determined by various classical methods. Although the probe technique has some limitation in electronegative plasmas, the plasma densities determined from OML theory compare well with those measured by the ion saturation current at the floating potential in the oxygen discharges. In addition, the EEDFs of inductively coupled Ar and oxygen plasmas are observed to be nearly Maxwellian at the pressure range of 1-40 mTorr.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2014

Interpretation of nanoparticle X-ray photoelectron intensities

X-ray photoelectron (XPS) intensities have been simulated for spherical core-shell nanoparticles (NPs) in different geometrical arrangements in order to investigate the validity of commonly made assumptions for the interpretation of XPS NP intensities. The single-sphere approximation is valid for a powder sample when all spatial coordinates of the NP positions are uncorrelated. Correlations along either the depth coordinate or the lateral coordinates lead to features in the angular distribution that provide information on these correlations. The XPS intensity is proportional to the surface-to-volume ratio of nanoparticles but only for NP sizes exceeding the inelastic mean free path of the photoelectrons.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2014

Interpreting impedance spectra of organic photovoltaic cells—Extracting charge transit and recombination rates

Impedance spectroscopy has been widely used to extract the electron-hole recombination rate constant in organic photovoltaic cells (OPVs). This technique is typically performed on OPVs held at open-circuit. Under these conditions, the analysis is simplified with recombination as the only pathway for the decay of excess charge carriers; transit provides no net change in the charge density. In this work, we generalize the application and interpretation of impedance spectroscopy for bulk heterojunction OPVs at any operating voltage. This, in conjunction with reverse bias external quantum efficiency measurements, permits the extraction of both recombination and transit rate constants. Using this approach, the transit and recombination rate constants are determined for OPVs with a variety of electron donor-acceptor pairings and compositions. It is found that neither rate constant individually is sufficient to characterize the efficiency of charge collection in an OPV. It is demonstrated that a large recombination rate constant can be accompanied by a large transit rate constant, thus fast recombination is not necessarily detrimental to OPV performance. Extracting the transit and recombination rate constants permits a detailed understanding of how OPV architecture and processing conditions impact the transient behavior of charge carriers, elucidating the origin of optimum device configurations.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2014

Interpreting the peak structures around 1800 MeV in the BES data on J/Ψ→φπ{sup +}π{sup −}, J/Ψ→γωφ

In this talk we present an interpretation for the experimental data available on two different processes, namely, J/Ψ→φπ{sup +}π{sup −}, J/Ψ→γωφ, which seem to indicate existence of two new resonances with the same quantum numbers (J{sup πc} = 0{sup ++}, I = 0) and savery similar mass (1800 MeV) but with very different decay properties. However, our studies show that the peak structure found in the ωφ invariant mass, in J/Ψ→γωφ, is a manifestation of the well known f{sub 0}(1710) while the cross section enhancement found in J/Ψ→φπ{sup +}π{sup −} is indeed a new f{sub 0} resonance with mass near 1800 MeV. We present an explanation for the different decay properties of these two scalar resonances.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗