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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Graph-based featurization methods for classifying small molecule compounds

For over a decade, drug-induced liver injury (DILI) has posed significant drawbacks in the synthesis and development of drugs and remains a consequential concern. With finite success within the existing preclinical models, DILI is one of the main causes of drug withdrawal or termination from the market. Particularly, this withdrawal occurs during the late stages of drug development (Kullak-Ublick, 2017). Since DILI is difficult to diagnose and treat, it has become an obstacle in the drug production market that in turn affects clinicians, pharmaceutical companies, and consumers. We propose a method for learning features of DILI-positive drugs based on the graphical relationships and patterns they possess within a network of biological databases. We also train various statistical and machine learning models on these learned features in order to classify the drugs as DILI-positive or negative. Our methods include Random Forest, Neural networks, and logistic regression classification. We utilize labeled DILI-positive and DILI-negative datasets, which were developed by the FDA and the National center for toxicological research, as well as additional literature datasets (Thakkar, 2020) in order to validate our results and assess our featurization and model accuracy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Shake-up and shake-off spectra in the electron capture decay of atomic $^7$Be

The most stringent laboratory-based experimental limits on the existence of sub-MeV sterile neutrinos are currently set by decay spectroscopy of radioactive $^7$Be embedded into superconducting sensors. The systematic uncertainties are dominated by the modeling of the electron shake-up and shake-off spectra that are not based on state-of-the-art atomic theory and do not include electron correlations or relativistic effects. We have used the multiconfiguration Dirac-Fock formalism to obtain correlated wavefunctions ab initio and compute all single and double shake processes in the electron capture decay of atomic $^7$Be. The simulations can explain some but not all of the observed spectral features, likely because the wave functions are modified by the Ta sensor material that the $^7$Be is embedded into. The new models also show that the L/K electron capture ratio of $^7$Be in Ta has previously been slightly underestimated revising the previous value of 0.070(7) to a new value of 0.0756(20).

Atomic Physics (physics.atom-ph)↗
DOE OSTI2025

Dispatch Manager for NEML2 Constitutive Model Calculations Embedded in MOOSE

This report describes the extended capabilities of the NEML2 constitutive modeling library, including a flexible and efficient work dispatching system designed to leverage both CPU and GPU resources. This enhancement addresses one of the primary computational challenges in large-scale simulations: the ability to distribute and execute batches of material model evaluations across heterogeneous computing devices. The new dispatch system introduces a modular set of dispatcher and scheduler classes that coordinate the flow of data and execution between devices. The dispatcher is responsible for efficiently packaging work, managing device-specific memory operations, and synchronizing results. This modularity allows for extensibility, making it straightforward to integrate additional computing backends in the future. From an implementation standpoint, the dispatcher system interfaces seamlessly with NEML2's existing models. They handle device-aware tensor operations, optimize memory transfers, and support asynchronous execution when applicable. This design ensures that batches of material points can be evaluated concurrently, substantially improving throughput compared to previous single-device or serial implementations. These improvements not only enhance the raw performance of NEML2 but also improve its usability in multiscale and high-fidelity simulations, where the simultaneous evaluation of large material point batches is critical. Benchmarks included in the report demonstrate the system’s scalability, highlighting its effectiveness when leveraging modern GPU architectures.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Report on ISR-1 High-Altitude Balloon Flight

To test small technologies at lower cost for space science applications, LANL has developed a small high altitude balloon payload that could, in the future, be regularly and inexpensively launched from LANL. A neutron detector, NEMO, was integrated to evaluate its performance in a space-like mixed-radiation environment and collect neutron data in the atmosphere. In collaboration with EES-14, a high-altitude balloon payload was launched from LANL Technical Area 51 on February 27, 2023 and April 17, 2023. For real-time geolocation, a SAM-M8Q M8 GNSS module was used to get position and time, and an Iridium RockBLOCK 9603 was used to communicate with the ground using the Iridium satellite fleet. These modules were all controlled using an Iteaduino Mega microcontroller board. Finally, a High Altitude Science Eagle Flight Computer with a temperature pressure sensor ran independently, writing data to an SD card. All of these modules were powered by a 5 mAh lithium polymer battery. The battery was attached to the bottom of the payload while the remaining electronics were embedded in the underside of the top of the payload. These modules were wired as seen in Figure 1-2. The Iteaduino Mega microcontroller board was programmed to use the RockBLOCK to send a message once every 10 minutes containing neutron and GPS data read off the NEMO and SAM M8Q, respectively. Once the message send attempt finished, the RockBLOCK would be slept for the rest of the 10 minute interval. The Eagle Flight Computer ran continuously throughout the flight, taking data every 6 seconds. The RockBLOCK message data was set up to be delivered from the Iridium satellite fleet to a website, where it was stored and parsed to create live maps and plots for analysis and balloon retrieval. The RockBLOCK message data was additionally configured to be sent to an email as a fail-safe. The payload was ground-tested successfully for over 50 hours, with multiple revisions occurring to best prepare for conditions at altitude and improve the software and firmware to fix any issues that cropped up with the data pipeline. Additional to the balloon payload, the flight had an attached iMet-4 radiosonde and Garmin T5 GPS Dog Collar. The radiosonde provided GPS and meteorological data. The T5 dog collar is used along with a Garmin Astro 430 to track the balloon at a range of up to 9 miles for retrieval. The balloon itself was initially a 1600 g meteorological balloon with an attached High Altitude Science parachute, both of which can be seen in Figure 1-3. After the first flight, the EES team swapped to a Rocketman parachute.

42 ENGINEERING↗
DOE OSTI2021

Architecture and properties of TCR fuel form

The fuel form developed for the Transformational Challenge Reactor demonstration program leverages recent advances in manufacturing, materials, and computational sciences, delivering a new architecture for production of high-performance microencapsulated nuclear fuels. The fuel consists of conventionally manufactured uranium nitride tristructural isotropic fuel particles embedded inside a 3D-printed silicon carbide matrix. Finally, this paper describes the overall architecture and manufacturing process for this fuel form, its properties and behavior, and the ongoing development activities.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Forces from Stochastic Density Functional Theory under Nonorthogonal Atom-Centered Basis Sets

We develop a formalism for calculating forces on the nuclei within the linear-scaling stochastic density functional theory (sDFT) in a nonorthogonal atom-centered basis set representation (Fabian et al. Wiley Interdiscip. Rev.: Comput. Mol. Sci. 2019, 9, e1412, 10.1002/wcms.1412) and apply it to the Tryptophan Zipper 2 (Trp-zip2) peptide solvated in water. We use an embedded-fragment approach to reduce the statistical errors (fluctuation and systematic bias), where the entire peptide is the main fragment and the remaining 425 water molecules are grouped into small fragments. We analyze the magnitude of the statistical errors in the forces and find that the systematic bias is of the order of 0.065 eV/Å (~1.2 × 10 -3 E h / a 0 ) when 120 stochastic orbitals are used, independently of system size. This magnitude of bias is sufficiently small to ensure that the bond lengths estimated by stochastic DFT (within a Langevin molecular dynamics simulation) will deviate by less than 1% from those predicted by a deterministic calculation.

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

TX$^2$: Transformer eXplainability and eXploration

The Transformer eXplainability and eXploration (Martindale & Stewart, 2021), or TX 2 software package, is a library designed for artificial intelligence researchers to better understand the performance of transformer models (Vaswani et al., 2017) used for sequence classification. The tool is capable of integrating with a trained transformer model and a dataset split into training and testing populations to produce an ipywidget (Project Jupyter Contributors, 2021) dashboard with a number of visualizations to understand model performance with an emphasis on explainability and interpretability. The TX 2 package is primarily intended to integrate into a workflow centered around Jupyter Notebooks (Kluyver et al., 2016), and currently assumes the use of PyTorch (Paszke et al., 2019) and Hugging Face transformers library (Wolf et al., 2020). The dashboard includes visualization and data exploration features to aid researchers, including an interactive UMAP embedding graph (McInnes et al., 2018) to understand classification clusters, a word salience map that can be updated as researchers alter textual entries in near real time, a set of tools to understand word frequency and importance based on the clusters in the UMAP embedding graph, and a set of traditional confusion matrix analysis tools.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

The Future of Visualization Technology Vis in Practice

Two decades ago, we witnessed an inflection point as graphics programming became commoditized. A comparable inflection point may be on the horizon, this time driven by the commoditization of immersive display technology with Augmented Reality (AR), Virtual Reality (VR), and Extended Reality (XR) technologies poised to redefine our interaction with data, environments, and collaboration. To drive the technology and its application, we should consider the lessons learned from successful large-scale immersive installations. Where these installations have thrived is by enhancing traditional workflows rather than entirely replacing them, supporting improved judgments of complex spaces, direct interaction in 3D, and embedding high-dimensional data.

AR/VR↗
DOE OSTI2022

ExaWind: Opportunity for Discovery

The predictive simulation of the dynamics of modern wind turbines and wind farms is a high-performance-computing (HPC) grand challenge. Modern wind turbines are the largest rotating machines in the world, with rotor diameters exceeding 200 meters, and with heights well into the atmospheric boundary layer. To address this grand challenge, the U.S. Department of Energy (DOE) Wind Energy Technologies Office and the Exascale Computing Project have been supporting the creation of the ExaWind modeling and simulation environment since 2016. ExaWind is composed of the incompressible-flow computational-fluid-dynamics (CFD) solvers AMR-Wind and Nalu-Wind and the wind-turbine-dynamics solver OpenFAST. In our hybrid CFD approach, Nalu-Wind unstructured-grid models resolve the near-body flow around the turbine; those models are embedded in, and two-way coupled to, an AMR-Wind structured-grid mesh through overset meshes. In this talk I give an overview of ExaWind software stack and describe how it is being used to address important problems in wind energy.

computational fluid dynamics↗
DOE OSTI2020

CP2K: An Electronic Structure and Molecular Dynamics Software Package - Quickstep: Efficient and Accurate Electronic Structure Calculations

CP2K is an open source electronic structure and molecular dynamics software package to perform atomistic simulations of solid-state, liquid, molecular and biological systems. It is especially aimed at massively-parallel and linear-scaling electronic structure methods and state-of-the-art ab-initio molecular dynamics simulations. Excellent performance for electronic structure calculations is achieved using novel algorithms implemented for modern high-performance computing systems. This review revisits the main capabilities of CP2K to perform efficient and accurate electronic structure simulations. The emphasis is put on density functional theory and multiple post-Hartree-Fock methods using the Gaussian and plane wave approach and its augmented all-electron extension. TDK has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 716142). VRR has been supported by the Swiss National Science Foundation in the form of Ambizione grant No. PZ00P2 174227 and RZK by the Natural Sciences and Engineering Research Council of Canada (NSERC) through Discovery Grants (RGPIN-2016-0505). GKS and CJM are supported by the US Department of Energy, Office of Science, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences, and Biosciences. UK based work was funded under the embedded CSE programme of the ARCHER UK National Supercomputing Service (http://www.archer.ac.uk), grants eCSE03-011, eCSE06-6, eCSE08-9, eCSE13-17 and the EPSRC (EP/P022235/1) grant “Surface and Interface Toolkit for the Materials Chemistry Community". Computational resources were provided by the Swiss National Supercomputing Centre (CSCS) and Compute Canada. The generous allocation of computing time on the FPGA-based supercomputer “Noctua" at PC2 is kindly acknowledged.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

CrossSim Inference Manual v2.0

Neural networks are largely based on matrix computations. During forward inference, the most heavily used compute kernel is the matrix-vector multiplication (MVM): $W \vec{x} $. Inference is a first frontier for the deployment of next-generation hardware for neural network applications, as it is more readily deployed in edge devices, such as mobile devices or embedded processors with size, weight, and power constraints. Inference is also easier to implement in analog systems than training, which has more stringent device requirements. The main processing kernel used during inference is the MVM.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Implementation of extrinsic cohesive zone model (ECZM) in 2D finite-discrete element method (FDEM) using node binding scheme

The combined finite-discrete element method (FDEM) has been widely used for rock fracturing simulations. Conventionally, FDEM is realized using the intrinsic cohesive zone model (ICZM); however, it has the drawback of artificial compliance and high computational expense. As a complement, the extrinsic cohesive zone model (ECZM) is seen to be realized in FDEM recently, whereas the node splitting scheme utilized is cumbersome. Here, within the framework of ICZM-based FDEM, we propose a node binding scheme to efficiently bind the pre-discretized finite elements and thus guarantee the continuum behavior of materials in the elastic stage. The yield surfaces, controlled by ECZM, are dynamically embedded by invoking the pre-inserted cohesive elements. The effectiveness and efficiency of the proposed approach are validated and tested by performing a suite of numerical experiments. Compared with ICZM-based FDEM, the proposed approach can correctly capture material deformation and reduce the computation cost. In contrast to the existing ECZM-based FDEM, the proposed approach can overcome the frequent and complex element topology updating. Finally, this work provides a novel perspective that fully inherits the advantages of both ICZM and ECZM, but circumvents their shortcomings, which guarantees a more efficient and effective simulation of brittle material evolution from continuum to discontinuum.

58 GEOSCIENCES↗
DOE OSTI2021

Engineering Graphene-Ceramic 3D Composite Foams by Freeze Drying

A 3D graphene foam produced by the chemical vapor deposition (CVD) technique is recognized as an effective nanofiller material, as it does not agglomerate in the matrix. Though CVD facilitates pristine graphene foam production, the method poses limitations in developing large-scale 3D graphene composite foams as advanced engineering materials. In this paper, a freeze-drying (FD) process is used to produce a composite 3D foam, which is a mixture of graphene nanoplatelet (GNP) and a low-temperature co-fired ceramic (LTCC). The freeze-dried GNP-LTCC reticulated 3D composite foam has an average pore size ranging from 70 to 100 μm. Pore size is varied by regulating the heat transfer rate during the freezing process using a thermally conductive aluminum (Al) mold and a thermally insulating acrylonitrile butadiene styrene (ABS) mold. Computational thermal modeling is used to visualize the heat transfer and its effect on foam pore size. Subsequently, a freeze-dried GNP-LTCC composite foam is embedded into the LTCC matrix to form a hierarchical assembly by a spark plasma technique without compromising the 3D structure of the FD foam. This study established that a simple, eco-friendly, and scalable processing methodology can produce advanced graphene-based 3D composite foams as the future tailorable nanofillers for designing multimatrix materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Explaining word embeddings with perfect fidelity: a case study in predicting research impact

The best-performing approaches for scholarly document quality prediction are based on embedding models. In addition to their performance when used in classifiers, embedding models can also provide predictions even for words that were not contained in the labelled training data for the classification model, which is important in the context of the ever-evolving research terminology. Although model-agnostic explanation methods, such as Local interpretable model-agnostic explanations, can be applied to explain machine learning classifiers trained on embedding models, these produce results with questionable correspondence to the model. We introduce a new feature importance method, Self-Model Entities Rated (SMER), for logistic regression-based classification models trained on word embeddings. We show that SMER has theoretically perfect fidelity with the explained model, as the average of logits of SMER scores for individual words (SMER explanation) exactly corresponds to the logit of the prediction of the explained model. Quantitative and qualitative evaluation is performed through five diverse experiments conducted on 50,000 research articles (papers) from the CORD-19 corpus. In conclusion, through an AOPC curve analysis, we experimentally demonstrate that SMER produces better explanations than LIME, SHAP and global tree surrogates.

Coarse-grained models↗
DOE OSTI2022

A reinforcement learning approach to long-horizon operations, health, and maintenance supervisory control of advanced energy systems

In this work, we develop a Reinforcement Learning (RL) approach to the supervisory control problem for advanced energy systems, such as novel nuclear reactors and other demand-driven, mission-critical, and component-health-sensitive energy plants. The inclusive problem landscape considered captures the stochastic confluence of plant performance, component health evolution, power demand from the grid, diverse maintenance actions, and operator-defined goals and constraints, all considered over meaningfully long-enough reasoning horizons. Key aspects of the proposed approach are a receding horizon control-inspired technique dictating time- or event-triggered supervisory policy (re-)constructions, as well as additional capability-enabling contributions such as timescale compression, to handle long reasoning horizons and uncertainty in parts of the problem, and practical yet demonstrably-effective handling of hybrid action spaces with continuous and discrete decision variables. The resulting algorithm consists of a simulation-based RL agent constructing stochastic supervisory control policies over nontrivial action spaces and for long horizons, applying the learned policy to the system for a much shorter interval, and perpetually repeating, to construct the next long-horizon policy. That next policy will only be applied, again, for a short interval, yet originally far-in-time events move progressively closer, their associated uncertainty decreases, and new events and aspects enter the reasoning horizon. The proposed methodology bridges fundamental receding horizon concepts with the unequivocally stronger and more scalable reasoning of contemporary RL. Numerical examples using Soft Actor–Critic Deep RL illustrate the operation and efficacy of the proposed technique for a power plant tasked with health-aware load following missions in a dynamic electricity market landscape.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Accurate simulation of surfaces and interfaces of ten FCC metals and steel using Lennard–Jones potentials

Abstract The earlier integration of validated Lennard–Jones (LJ) potentials for 8 fcc metals into materials and biomolecular force fields has advanced multiple research fields, for example, metal–electrolyte interfaces, recognition of biomolecules, colloidal assembly of metal nanostructures, alloys, and catalysis. Here we introduce 12-6 and 9-6 LJ parameters for classical all-atom simulations of 10 further fcc metals (Ac, Ca (α), Ce (γ), Es (β), Fe (γ), Ir, Rh, Sr (α), Th (α), Yb (β)) and stainless steel. The parameters reproduce lattice constants, surface energies, water interfacial energies, and interactions with (bio)organic molecules in 0.1 to 5% agreement with experiment, as well as qualitative mechanical properties under standard conditions. Deviations are reduced up to a factor of one hundred in comparison to earlier Lennard–Jones parameters, embedded atom models, and density functional theory. We also explain a quantitative correlation between atomization energies from experiments and surface energies that supports parameter development. The models are computationally very efficient and applicable to an exponential space of alloys. Compatibility with a wide range of force fields such as the Interface force field (IFF), AMBER, CHARMM, COMPASS, CVFF, DREIDING, OPLS-AA, and PCFF enables reliable simulations of nanostructures up to millions of atoms and microsecond time scales. User-friendly model building and input generation are available in the CHARMM-GUI Nanomaterial Modeler. As a limitation, deviations in mechanical properties vary and are comparable to DFT methods. We discuss the incorporation of reactivity and features of the electronic structure to expand the range of applications and further increase the accuracy.

Kanhaiya, Krishan (ORCID:0000000236222655)↗
DOE OSTI2020

Spike-based graph centrality measures

We derive several spike-based routines that compute or establish bounds on radial centrality measures for undirected graphs and trees without the use of matrix multiplication. These spike-based centrality measures utilize a direct embedding of graph nodes and edges into neurons and synapses, can be implemented with static synapses or plastic synapses, and rely on minimal post-processing of spike rasters. This work contributes to the growing set of graphical applications for neuromorphic hardware.

Hamilton, Kathleen↗
DOE OSTI2026

Commuting embeddings for parallel strategies in non-local games

Non-local games provide a versatile framework for probing quantum correlations and for benchmarking the power of entanglement. In finite dimensions, the standard method for playing several games in parallel requires a tensor product of the local Hilbert spaces, which scales additively in the number of qubits. In this work, we show that this additive cost can be reduced by exploiting algebraic embeddings. We introduce two forms of compressions. First, when a referee selects one game from a finite collection of games at random, the game quantum strategy can be implemented using a maximally entangled state of dimension equal to the largest individual game, thereby eliminating the need for repeated state preparations. Second, we establish conditions under which several games can be played simultaneously in parallel on fewer qubits than the tensor product baseline. These conditions are expressed in terms of commuting embeddings of the game algebras. Moreover, we provide a constructive framework for building such embeddings. Using tools from Lie theory, we show that aligning the various game algebras into a common Cartan decomposition enables such a qubit reduction. Beyond the theoretical contribution, our framework casts NLGs as algebraic primitives for distributed and resource-constrained quantum computations and suggested NLGs as a comparable device-independent dimension witness.

Commuting embeddings↗