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At least 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
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 OSTI2026

Diagonal isometric form for tensor network states in two dimensions

Isometric tensor network states (isoTNS) generalize the isometric form of the one-dimensional matrix product states to tensor networks in two and higher dimensions. Here, we introduce an alternative isometric form for isoTNS by incorporating auxiliary tensors to represent the orthogonality hypersurface. We implement the time evolving block decimation algorithm on this new isometric form and benchmark the method by computing ground states and the real time evolution of the transverse field Ising model in two dimensions on large square lattices of up to 1250 sites. Our results demonstrate that isoTNS can efficiently capture the entanglement structure of two-dimensional area law states. The short-time dynamics is also accurately reproduced even at the critical point. Our isoTNS formulation further allows for a natural extension to different lattice geometries, such as the honeycomb or kagome lattice.

2-dimensional systems↗
DOE OSTI2025

Explainable AI for Multivariate Time Series Pattern Exploration: Latent Space Visual Analytics With Temporal Fusion Transformer and Variational Autoencoders in Power Grid Event Diagnosis

Detecting and analyzing complex patterns in multivariate time-series data is crucial for decision-making in urban and environmental system operations. However, challenges arise from the high dimensionality, intricate complexity, and interconnected nature of complex patterns, which hinder the understanding of their underlying physical processes. Existing AI methods often face limitations in interpretability, computational efficiency, and scalability, reducing their applicability in real-world scenarios. This paper proposes a novel visual analytics framework that integrates two generative AI models, Temporal Fusion Transformer (TFT) and Variational Autoencoders (VAEs), to reduce complex patterns into lower-dimensional latent spaces and visualize them in 2D using dimensionality reduction techniques such as PCA, t-SNE, and UMAP with DBSCAN. These visualizations, presented through coordinated and interactive views and tailored glyphs, enable intuitive exploration of complex multivariate temporal patterns, identifying patterns’ similarities and uncover their potential correlations for a better interpretability of the AI outputs. The framework is demonstrated through a case study on power grid signal data, where it identifies multi-label grid event signatures, including faults and anomalies with diverse root causes. Additionally, novel metrics and visualizations are introduced to validate the models and assess the performance, efficiency, and consistency of latent maps generated by VAE, which have been utilized in prior studies for latent space cartography and used as a benchmark in this study, and the emerging TFT architecture under various configurations. These analyses provide actionable insights for model parameter tuning and reliability improvements. Comparative results highlight that TFT achieves shorter run times and superior scalability to diverse time-series data shapes compared to VAE. This work advances fault diagnosis in multivariate time series, fostering explainable AI to support critical system operations.

Explainable AI↗
DOE OSTI2022

Sensitivity Coefficients Calculated for the Prompt Neutron Decay Constant at or Near Delayed Critical [Abstract]

Experimenters at Los Alamos National Laboratory (LANL) measure the prompt neutron decay constant for many experiments at the National Criticality Experiments Research Center (NCERC) to infer reactivity and the effective neutron multiplication factor. These quantities are very important for nuclear criticality safety and validating nuclear data. Uncertainty in measures of criticality of an experimental configuration can be determined prior to physically performing the experiment by applying first order perturbation theory to Monte Carlo codes, such as MCNP®. The first order perturbation theory produces first derivatives of some nuclear parameter to nuclear data (e.g., cross section data). This first derivative is commonly referred to as a sensitivity coefficient. Currently, the MCNP® software has the capability of computing effective neutron multiplication factor sensitivity coefficients to cross section data. This work builds off of this MCNP® capability and the first order perturbation theory to provide a method of calculating sensitivity coefficients for the prompt neutron decay constant at or near delayed critical to cross section data. The prompt neutron decay constant sensitivity coefficient calculated in this work does not depend on any modification of the MCNP® source code. Prompt neutron decay constant sensitivity coefficient calculations can be used to infer reactivity and effective neutron multiplication factor sensitivity coefficient values as well. By investigating the trends of prompt neutron decay constant sensitivity coefficients for nuclide-reaction pairs across energy spectra, experiments can be designed to maximize or minimize the uncertainty in the prompt neutron decay constant in a particular energy region, which can lead to further optimization studies. Prompt neutron decay constant sensitivity coefficients will be calculated for the Jezebel benchmark. Subsequently, these sensitivity coefficients will be used in a data assimilation process to determine if there is or are optimal experiments that can be performed to provide insight into adjustments of uncertain/inaccurate cross section data. Specifically, the effect of the prompt neutron decay constant sensitivity coefficients on the nuclear data-induced uncertainty in the effective neutron multiplication factor will be examined

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

MCNP ® Code V.6.3.0 Release Notes

The Monte Carlo N-Particle ® (MCNP ® ) code is a general-purpose, continuous-energy, generalized geometry, time-dependent, radiation transport code developed by the MCNP development team. The MCNP calculations provide predictive capabilities that can replace expensive or impossible-to perform experiments. Specific application problems include simulations of experimental diagnostics, intrinsic radiation, radiation detection and measurement, criticality safety, nuclear threat reduction and response, radiation health protection, nuclear weapons effects, and nuclear forensics. This MCNP code, version 6.3.0, follows the MCNP6.2.0 version. Since the release of MCNP6.2.0, many changes have been made to the MCNP code. These changes include new or improved features, a new build system, code enhancement and modernization, and bug fixes. The MCNP code, version 6.3.0, theory and user input information is documented in MCNP ® Code Version 6.3.0 Theory & User Manual, the build guidance for various platforms is documented in MCNP ® Code Version 6.3.0 Build Guide, and the verification and validation testing for various application benchmark test suites is documented in MCNP ® Code Version 6.3.0 Verification & Validation Testing.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

IER 501: Pulsed Neutron Die-Away Experiments at LLNL [Slides]

This presentation notes the purpose of Pulsed Neutron Die Away (PNDA) for Thermal Neutron Scattering Law (TSL) validation. PNDA for TSL validation does not require fissile material. It consists of simple target shapes and compositions and is only sensitive to absorption and scattering of target medium. Well conducted experiments have uncertainties of 0.1% - 0.5%. This presentation also covers Pulsed Neutron Die Away Experiments, while noting previous experiments such as the FY22 PNDA Experiments. The presentation concludes with FY23 benchmarking efforts and a timeline to FY24.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Incorporating Full Elastodynamic Effects and Dipping Fault Geometries in Community Code Verification Exercises for Simulations of Earthquake Sequences and Aseismic Slip (SEAS)

ABSTRACT Numerical modeling of earthquake dynamics and derived insight for seismic hazard relies on credible, reproducible model results. The sequences of earthquakes and aseismic slip (SEAS) initiative has set out to facilitate community code comparisons, and verify and advance the next generation of physics-based earthquake models that reproduce all phases of the seismic cycle. With the goal of advancing SEAS models to robustly incorporate physical and geometrical complexities, here we present code comparison results from two new benchmark problems: BP1-FD considers full elastodynamic effects, and BP3-QD considers dipping fault geometries. Seven and eight modeling groups participated in BP1-FD and BP3-QD, respectively, allowing us to explore these physical ingredients across multiple codes and better understand associated numerical considerations. With new comparison metrics, we find that numerical resolution and computational domain size are critical parameters to obtain matching results. Codes for BP1-FD implement different criteria for switching between quasi-static and dynamic solvers, which require tuning to obtain matching results. In BP3-QD, proper remote boundary conditions consistent with specified rigid body translation are required to obtain matching surface displacements. With these numerical and mathematical issues resolved, we obtain excellent quantitative agreements among codes in earthquake interevent times, event moments, and coseismic slip, with reasonable agreements made in peak slip rates and rupture arrival time. We find that including full inertial effects generates events with larger slip rates and rupture speeds compared to the quasi-dynamic counterpart. For BP3-QD, both dip angle and sense of motion (thrust versus normal faulting) alter ground motion on the hanging and foot walls, and influence event patterns, with some sequences exhibiting similar-size characteristic earthquakes, and others exhibiting different-size events. These findings underscore the importance of considering full elastodynamics and nonvertical dip angles in SEAS models, as both influence short- and long-term earthquake behavior and are relevant to seismic hazard.

Geochemistry & Geophysics↗
DOE OSTI2021

A Verification of Flux Sensitivity Estimates Using the MCNP Tally Perturbation Tool

Nuclear data is commonly used in applications such as nuclear nonproliferation, safeguards, and criticality safety. More specifically, nuclear data is used in predictive simulation codes like the Monte-Carlo N-Particle (MCNP ® ) transport code, Serpent, and similar radiation transport codes. The improvement of nuclear data enables more precise and accurate simulations, which result in higher fidelity designs and reduced operational/procedural costs. Therefore, the improvement of nuclear data is of paramount importance across the nuclear community. Nuclear data is improved and validated through integral benchmark experiments. The design of benchmark experiments is an extensive process; therefore, these experiments are often optimized on multiple characteristics, including sensitivity to the nuclear data, during the design process. Sensitivity is a measure of how much a quantity changes due to changes in independent variables such as experimental configuration. An experimental design that has a larger sensitivity to the nuclear data of interest will have a larger impact on the accuracy and precision of the validated data. Past integral benchmark experiments have primarily used the effective multiplication factor ($k_{eff}$) as the predominant measured quantity; however, experiments designed with other quantities in mind would be able to optimize on validating different areas of the nuclear data. A primary goal of the EUCLID project is to design, constrain, and reduce compensating errors in experiments focused on quantities other than $k_{eff}$ to better validate nuclear data across the board. Currently, there is a capability in MCNP to easily calculate the sensitivity of $k_{eff}$ to specific nuclear data of numerous reactions types and isotopes (KSEN card); however, the sensitivity of other quantities must be estimated in more strenuous manners. For example, the perturbation feature (PERT card) of MCNP can be used to estimate first-order sensitivities of some response in fixed source simulations. A recent announcement revealed that the first- and second-order perturbation features in previous releases of MCNP contained a bug. It was identified that particles were being scored into the wrong energy bin. The bug is in the most recent public release (MCNP6.2); however, a patch has been added to the most up to date version (MCNP6.2.2) that has not been released publicly. A direct comparison of the PERT card results for an F4 (neutron flux averaged over a cell) tally before and after the patch are shown in figure 1. All simulations used in the sensitivity estimates in this report were performed with MCNP6.2.2. This work verifies the patched MCNP perturbation tool by comparing first order sensitivities made using the PERT card to estimates made using manual perturbation of the compact ENDF (ACE) files.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Creating Novel Gas Separation Constructs by Manipulating Free-Volume Distributions in Polymer-Grafted Nanoparticles

Polymer membranes are critical to sustainability applications, and in this work we focused on one key application, the efficient separation of gas mixtures. Despite their widespread use, important challenges remain in the use of polymer membranes in this context – i.e., finding membrane materials with selectively improved transport of the desired mixture component(s) while possessing enhanced strength and improved aging behavior relative to the best current benchmarks. The important separation figures of merit are the gas flux, which is proportional to the permeability, P i = D i × S i (Di is the gas diffusivity and S i its solubility coefficient) and selectivity (i.e., gas purity,α ij = P i /P j ). The goal is to simultaneously maximize P i and α ij . Most research to date has empirically targeted the development of new glassy polymers with improved separation performance. These include thermally rearranged (TR) polymers and polymers of intrinsic microporosity (PIMs).

36 MATERIALS SCIENCE↗
DOE OSTI2025

Verified, Archived Library of Inputs and Data (VALID) Online Repository [Slides]

VALID continues to serve as a library of high-quality models used for evaluating SCALE and nuclear data. Large number of cases are in the pipeline. Online repository is available for use. Future plans include simplifying the process for adding cases, including additional models outside the ICSBEP Handbook (HTC models, DNCSH models, International Handbook of Evaluated Reactor Physics Benchmark Experiments [IRPhE], SINBAD, etc.), and increasing the availability of models and results for external users.

KENO↗
DOE OSTI2026

Life Cycle Inventories and Data Gap Analysis for Rare Earth Elements: Neodymium and Dysprosium from Mining to Magnets

The United States demand for Neodymium-Iron-Boron (NdFeB) magnets, produced from rare earth elements (REEs) such as (Nd) and Dysprosium (Dy), far exceeds its nascent domestic production capacity, rendering it reliant on vulnerable global supply chains dominated by China. To guide research and development investments in securing U.S. REE supply, defensible benchmark metrics across environmental, economic, and social dimensions are needed. In this study, we built globally-representative, process-based cradle-to-cradle life cycle inventories for Nd and Dy in NdFeB magnets lifecycles, encompassing primary material acquisition, beneficiation, smelting and refining, metal processing, specialty alloy and chemical transformation, subcomponent manufacturing, consumer application (use phase) and end-of-life management. We carried out detailed literature review, and applied process engineering principles to build industry-representative upscaled life cycle inventories for both metals. We used these models to conduct bottom-up literature review and gap analysis on existing literature, compilation of data sources for each life cycle stage (and transformations where necessary), and a preliminary technoeconomic analysis (TEA)/life cycle costing analysis (LCCA). Findings from this work emphasize the need for metal specific, representative REE LCIs to establish robust benchmarks for advancing sustainable REE technologies and guiding R&D in REE supply chains.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Beam loss modeling and mitigation due to intra-beam stripping

Intra-Beam Stripping (IBS) is a critical beam loss mechanism in high-intensity H- linacs and presents a significant limitation to increasing beam power. This work presents a computational framework to evaluate and mitigate IBS-induced beam loss along the Spallation Neutron Source (SNS) LINAC. Our calculation is based on an analytic theory and involves evaluation of a 9D integral using the Monte-Carlo technique. We first benchmarked our calculations against simplified, analytically solvable cases. We then applied our algorithm to Gaussian bunches with a known probability density function (PDF). We next expanded our algorithm to arbitrary bunch distributions using the Neural Spline Flow (NSF) models trained on PyORBIT tracking data. In the future, we plan to validate our algorithm experimentally and apply it to design IBS mitigation strategies.

Nln, Shivam [ORNL]↗
DOE OSTI2023

Zirconium Scattering Sensitivity in Neutron Transport Calculations of Multiplying Systems [Slides]

This presentation touches on motivation and applications of Zirconium. Performance of evaluated libraries on double and quasi differential experiments is considered. Additionally, the performance of evaluated libraries in select ICSBEP benchmarks is considered. Provided is a detailed look in the performance of evaluated libraries in preliminary benchmark configurations for IER-516: Zirconium Test Assembly (ZTA). Conclusions and recommended experiments to satisfy Zirconium scattering nuclear data needs are presented.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

HALEU Experiments [Slides]

The DNCSH, through the HALEU Availability Program, needs publicly available benchmarks supporting Advanced Reactor Fuel Cycle and Transportation. LANL is uniquely positioned to support this goal, with its past experiments at TA-18, planned experiments at NCERC, experiment design capability, and ties to industry. The Deimos experiment will be executed soon and serve as a premier benchmark for the program. LANL expects to submit proposals to the call when issued (soon). Additional experiments supporting DNCSH have already been designed

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Recent Advances in Scalable, High‐Mass Loaded Electrodes for Grid‐Scale Energy Storage

Abstract The increasing electrification of daily life as well as the intermittent characteristic of renewable energy sources require viable solutions for grid‐scale energy storage. Critical considerations for grid storage applications are electrode mass loading and electrode thickness as these features govern battery pack energy density, an important factor in determining manufacturing costs. For this reason, there is increased interest in finding new ways of creating electrodes with high mass loading. In this review, various high‐mass loading fabrication approaches are considered for positive electrode materials used in batteries. The benchmark used for high mass loading is above 20 mg cm −2 , which is higher than the practical limit of conventional tape‐cast electrodes. Several different electrode approaches are described including templating, laser patterning, direct ink writing, and electrodeposition. A variety of materials are covered with the most prominent being LiFe(PO 4 ) (LFP), LiCoO 2 (LCO), and MnO 2 . In research to date, scalable electrochemical performance has been achieved with mass loadings over 100 mg cm −2 . Areal capacities as high as 14.7 mAh cm −2 at 1.82 mA cm −2 have been achieved in non‐aqueous electrolytes and 9.8 mAh cm −2 at 10 mA cm −2 in aqueous electrolytes. These results establish that the mass loading of electrodes can be scaled up without compromising their electrochemical properties.

White, Makena [Department of Materials Science and↗
DOE OSTI2022

Disentangling the role of bond lengths and orbital symmetries in controlling $T_c$ of optimally doped YBa 2 Cu 3 O 7

Optimally doped YBa 2 Cu 3 O 7 (YBCO) has a high critical temperature, at 92 K. It is largely believed that Cooper pairs form in YBCO and other cuprates because of spin fluctuations, but the issue and the detailed mechanism are far from settled. In the present work, we employ a state-of-the-art first-principles ability to compute both the low- and high-energy spin fluctuations in optimally doped YBCO. We benchmark our results against recent inelastic neutron scattering and resonant inelastic x-ray scattering measurements. Further, we use strain as an external parameter to modulate the spin fluctuations and superconductivity. We disentangle the roles of barium-apical oxygen hybridization, interlayer coupling, and orbital symmetries by applying an idealized strain, and also a strain with a fully relaxed structure. We show that shortening the distance between Cu layers is conducive to enhanced Fermi surface nesting, which increases spin fluctuations and drives up $T_c$. However, when the structure is fully relaxed, electrons flow to the $d_{z^2}$ orbital as a consequence of a shortened Ba-O bond, which is detrimental for superconductivity.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Iterative quantum optimization of spin glass problems with rapidly oscillating transverse fields

In this work, we introduce a new iterative quantum algorithm, called Iterative Symphonic Tunneling for Satisfiability problems (IST-SAT), which solves quantum spin glass optimization problems using high-frequency oscillating transverse fields. IST-SAT operates as a sequence of iterations, in which bitstrings returned from one iteration are used to set spin-dependent phases in oscillating transverse fields in the next iteration. Over several iterations, the novel mechanism of the algorithm steers the system toward the problem ground state. We benchmark IST-SAT on sets of hard MAX-3-XORSAT problem instances with exact state vector simulation, and report polynomial speedups over Trotterized adiabatic quantum computation and the best known semi-greedy classical algorithm. When IST-SAT is seeded with a sufficiently good initial approximation, the algorithm converges to exact solution(s) in a polynomial number of iterations. Our numerical results identify a critical Hamming radius, or quality of initial approximation, where the time-to-solution crosses from exponential to polynomial scaling in problem size. This work proposes IST-SAT a new quantum algorithm, which improves upon solutions obtained from initial classical or quantum optimization algorithms. The steering mechanism we introduce through IST-SAT presents a new path toward achieving quantum advantage in optimization.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Identifying Dense NiSe 2 /CoSe 2 Heterointerfaces Coupled with Surface High-Valence Bimetallic Sites for Synergistically Enhanced Oxygen Electrocatalysis

Constructing heterostructures with abundant interfaces is essential for integrating the multiple functionalities in single entities. Herein, the synthesis of NiSe 2 /CoSe 2 heterostructures with different interfacial densities via an innovative strategy of successive ion injection is reported. The resulting hybrid electrocatalyst with dense heterointerfaces exhibits superior electrocatalytic properties in an alkaline electrolyte, superior to other benchmarks and precious metal catalysts. Advanced synchrotron techniques, post structural characterizations, and density functional theory (DFT) simulations reveal that the introduction of atomic-level interfaces can lower the oxidation overpotential of bimetallic Ni and Co active sites (whereas Ni$^{2+}$ can be more easily activated than Co$^{2+}$) and induce the electronic interaction between the core selenides and surface in situ generated oxides/hydroxides, which play a critical role in synergistically reducing energetic barriers and accelerating reaction kinetics for catalyzing the oxygen evolution. Hence, the heterointerface structure facilitates the catalytic performance enhancement via increasing the intrinsic reactivity of metallic atoms and enhancing the synergistic effect between the inner selenides and surface oxidation species. This work not only complements the understanding on the origins of the activity of electrocatalysts based on metal selenides, but also sheds light on further surface and interfacial engineering of advanced hybrid materials.

36 MATERIALS SCIENCE↗