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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

A Proxy Method to Bridge LCA Data Gaps Using Automated Material Classification and Probabilistic Under-Specification

Life cycle assessments (LCAs) are essential for understanding the environmental impacts of material production. However, gaps in life cycle inventory (LCI) data for material and chemical inputs present a key challenge for LCA practitioners, especially in the early design stages. Strategies for filling in these gaps require additional time and expertise, which can hinder the LCA’s completion. This study combined automatic material classification and probabilistic under-specification to create a time-efficient method to fill material LCI data gaps. To illustrate the proposed method, proxy environmental impact distributions were generated using publicly available material LCI data classified into the ChemOnt chemical taxonomy using the open-source chemical classification software ClassyFire. Input materials with data gaps were then classified into the same taxonomy, where proxy environmental impact values could be selected from the available distributions to quickly fill in any data gaps. Although these methods were applied to classify material production processes available in the Federal LCA Commons and Ecoinvent databases, they can be applied to any LCA database. This study shows that classifying materials by their chemical structure produces taxonomies with increased granularity relative to industrial classification, improving the ability of under-specified proxy data to be used for differentiating the environmental impacts of competing designs.

biological databases↗
DOE OSTI2025

Resolving the Coverage Dependence of Surface Reaction Kinetics with Machine Learning and Automated Quantum Chemistry Workflows

Microkinetic models for catalytic systems require estimation of many thermodynamic and kinetic parameters that can be calculated for isolated species and transition states using ab initio methods. However, the presence of nearby coadsorbates on the surface can dramatically alter these thermodynamic and kinetic parameters causing them to be dependent on species coverage fractions. As there are combinatorially many coadsorbed configurations on the surface, computing the coverage dependence of these parameters is far less straightforward. We present a framework for generating and applying machine learning models to predict coverage-dependent parameters for microkinetic models. Our toolkit enables automatic calculation and evaluation of coadsorbed configurations allowing us to sample 2,000 coadsorbed adsorbates and transition states (TSs) for a diverse set of 9 reactions on Cu(111), a challenging surface, with four possible coadsorbates. This dataset was then used to train subgraph isomorphic decision trees (SIDTs) to predict the stability and association energy of configurations. We were able to achieve mean absolute errors (MAEs) of 0.106 eV on adsorbates, 0.172 eV on TSs, and due to natural error cancellation in SIDTs for relative properties, 0.130 eV on reaction energies and 0.180 eV on activation barriers. In conclusion, we describe how to use these models to predict coverage-dependent corrections for adsorbates and TSs and demonstrate on H*, HO*, and O* comparing the generated SIDT model with an iteratively refined version.

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

Scalable fabrication of an array-type fixed-target device for automated room temperature X-ray protein crystallography

X-ray crystallography is one of the leading tools to analyze the 3-D structure, and therefore, function of proteins and other biological macromolecules. Traditional methods of mounting individual crystals for X-ray diffraction analysis can be tedious and result in damage to fragile protein crystals. Furthermore, the advent of multi-crystal and serial crystallography methods explicitly require the mounting of larger numbers of crystals. To address this need, we have developed a device that facilitates the straightforward mounting of protein crystals for diffraction analysis, and that can be easily manufactured at scale. Inspired by grid-style devices that have been reported in the literature, we have developed an X-ray compatible microfluidic device that can be used to trap protein crystals in an array configuration, while also providing excellent optical transparency, a low X-ray background, and compatibility with the robotic sample handling and environmental controls used at synchrotron macromolecular crystallography beamlines. At the Stanford Synchrotron Radiation Lightsource (SSRL), these capabilities allow for fully remote-access data collection at controlled humidity conditions. Furthermore, we have demonstrated continuous manufacturing of these devices via roll-to-roll fabrication to enable cost-effective and efficient large-scale production.

chemical engineering↗
DOE OSTI2024

ETHOS: An automated framework to generate multi-fidelity constitutive data tables and propagate uncertainties to hydrodynamic simulations

Accurate constitutive data, such as equations of state and plasma transport coefficients, are necessary for reliable hydrodynamic simulations of plasma systems such as fusion targets, planets, and stars. Here, we develop a framework for automatically generating transport-coefficient tables using a parameterized model that incorporates data from both high-fidelity sources (e.g., density functional theory calculations and reference experiments) and lower-fidelity sources (e.g., average-atom and analytic models). The framework incorporates uncertainties from these multi-fidelity sources, generating ensembles of optimally diverse tables that are suitable for uncertainty quantification of hydrodynamic simulations. We illustrate the utility of the framework with magnetohydrodynamic simulations of magnetically launched flyer plates, which are used to measure material properties in pulsed-power experiments. We explore how changes in the uncertainties assigned to the multi-fidelity data sources propagate to changes in simulation outputs and find that our simulations are most sensitive to uncertainties near the melting transition. The presented framework enables computationally efficient uncertainty quantification that readily incorporates new high-fidelity measurements or calculations and identifies plasma regimes where additional data will have high impact.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Automated Hybrid Variance Reduction on Advanced Architectures in the Shift Monte Carlo Code

Monte Carlo transport methods are the most accurate schemes for solving problems with complex energy and spatial features, but they come with a high computational cost. Although hybrid methods have enabled the use of Monte Carlo transport for a large class of problems, they still require significant computing resources. Modern multicore CPUs with large numbers of compute cores and graphical processing units (GPUs) provide opportunities to optimize the memory and run-time costs of hybrid Monte Carlo methods. This paper documents the development and analysis of three Monte Carlo transport algorithms that support hybrid transport using the consistent adjoint-driven importance sampling (CADIS) and forward-weighted CADIS methods in the Shift Monte Carlo code: history-based transport using static and dynamic threading on multicore CPUs and event-based transport enabling weight window tracking on GPUs. The results are shown for two challenging hybrid problems on the Frontier supercomputer at the Oak Ridge Leadership Computing Facility. The results show that all three methods yield good performance and enable solutions of difficult fixed-source transport problems in less than 2 min on 20 nodes of Frontier. Dynamic threading was observed to give up to 20% better scaling behavior than static threading. Moreover, the AMD Instinct 250X GPU was found to give 9 to 11 times greater throughput per graphics compute die than the best CPU performance. In conclusion, additional opportunities for optimization of hybrid transport on GPUs are discussed.

Denovo↗