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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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Proteome-Scale Tissue Mapping Using Mass Spectrometry Based on Label-Free and Multiplexed Workflows
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Refinement and demonstration of a coupled BISON-Griffin workflow for designing targeted TRISO transient experiments in TREAT
The U.S. nuclear industry is expected to deploy tristructural isotropic (TRISO) particle fuel technologies for commercial reactors within the next decade. In previous work, we defined a preliminary transient design space for TRISO fuels, identified potential gaps in the available data, and began to develop multiphysics modeling tools that could be applied to design targeted Transient Reactor Test Facility (TREAT) experiments to fill these gaps. Here, this work builds on that foundation by (1) updating BISON fuel performance and Griffin reactor physics models to reflect the current TREAT experiment tube and capsule designs, (2) coupling the codes to improve the accuracy and usability of the transient design analyses, and (3) demonstrating their use over an expanded design space that includes fuel burnup. The simulated mechanical responses of the TRISO particles were complex functions of fission product accumulation, fission gas release, and irradiation-induced dimensional change in the pyrolytic carbon layers. The predicted tangential stresses in the particles' silicon carbide layers were least compressive for preheated tests involving fresh fuels but remained compressive throughout the ranges of temperature, heat rate, and burnup considered in this work. Finally, comparisons between the potential TREAT transients and historical test reactor irradiations showed that the TREAT tests would produce significantly lower average energy deposition rates, yielding less severe transients with greater relevance to near-term commercial applications. The use of these predictive capabilities has the potential to increase the value of each test, improving the overall efficiency and cost-effectiveness of transient testing for TRISO and other advanced fuels.
Application of risk-informed closed-loop field development workflow to elucidate the evolution of uncertainties
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qlty: Handling large tensors in scientific imaging deep-learning workflows
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Network for knowledge Organization (NEKO): An AI knowledge mining workflow for synthetic biology research
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Geochemical and Microstructural Characterization of Shale Rock: A Workflow for Site Evaluation for Energy–Fluids Storage
Shales are a central component of petroleum systems, as source, seal, and unconventional reservoir rocks. Unlike traditional unconventional shale, a newly emerging Caney Shale play is regarded as an unconventional of unconventional shales (UUS), due to its high content of fine-grained materials, lower reservoir porosity, dominance of nanopores, and a scarcity of visible natural fractures. Other developed unconventional shales such as the Woodford and Barnett have larger average pore size, higher porosity, and extensive natural fractures at core scale that contribute to reservoir quality. In this work, the Caney Shale was cored in entirety to characterize its interbedded ductile and reservoir intervals and establish criteria for their recognition. Representative ductile and reservoir intervals known as D2 and R3, respectively, were selected for detailed analysis including variations in elemental composition, mineralogy, facies, and the presence of natural fractures at well and core scales using X-ray fluorescence, X-ray diffraction, and thin section and core description. Characteristics of microstructure and microgeochemistry at nano- and microscales are compared using field emission scanning electron microscopy with energy dispersive spectroscopy and lowpressure nitrogen adsorption isotherms with fractal dimension analysis. The ductile (detrital clay-rich) member D2 is characterized by higher concentrations of Ti and Al and lower Si, while the reservoir R3 (possibly biogenic silica-accumulated) is characterized by lower Ti and Al and higher Si. Eight mixed carbonate–siliciclastic facies are recognized, and R3 shows a higher heterogeneity of facies stacking and average fracture abundance than D2. Further, R3 shows a more microscopically heterogeneous fabric/texture of matrix and a higher microporosity than D2 that has a higher pore surface heterogeneity. A fundamental understanding of the compositional and microstructural characteristics of UUS and further ductile/reservoir intervals will allow for a better assessment of reservoir quality, more effective production of hydrocarbons, and optimized selection of safe caprocks in carbon sequestration and subsurface hydrogen storage.
Enhanced Exploration of Protein Conformational Space through Integration of Ultra-Coarse-Grained Models to Multiscale Workflows
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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.
Leveraging Uncertainty from Deep Learning for Trustworthy Material Discovery Workflows
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Demonstration of an AI-driven workflow for autonomous high-resolution scanning microscopy
Abstract Modern scanning microscopes can image materials with up to sub-atomic spatial and sub-picosecond time resolutions, but these capabilities come with large volumes of data, which can be difficult to store and analyze. We report the Fast Autonomous Scanning Toolkit (FAST) that addresses this challenge by combining a neural network, route optimization, and efficient hardware controls to enable a self-driving experiment that actively identifies and measures a sparse but representative data subset in lieu of the full dataset. FAST requires no prior information about the sample, is computationally efficient, and uses generic hardware controls with minimal experiment-specific wrapping. We test FAST in simulations and a dark-field X-ray microscopy experiment of a WSe 2 film. Our studies show that a FAST scan of <25% is sufficient to accurately image and analyze the sample. FAST is easy to adapt for any scanning microscope; its broad adoption will empower general multi-level studies of materials evolution with respect to time, temperature, or other parameters.
Demonstration of an AI-driven workflow for dynamic x-ray spectroscopy
X-ray absorption near edge structure (XANES) spectroscopy is a powerful technique for characterizing the chemical state and symmetry of individual elements within materials, but requires collecting data at many energy points which can be time-consuming. While adaptive sampling methods exist for efficiently collecting spectroscopic data, they often lack domain-specific knowledge about the structure of XANES spectra. Here we demonstrate a knowledge-injected Bayesian optimization approach for adaptive XANES data collection that incorporates understanding of spectral features like absorption edges and pre-edge peaks. We show this method accurately reconstructs the absorption edge of XANES spectra using only 15–20% of the measurement points typically needed for conventional sampling, while maintaining the ability to determine the x-ray energy of the sharp peak after the absorption edge with errors less than 0.03 eV, the absorption edge with errors less than 0.1 eV; and overall root-mean-square errors less than 0.005 compared to traditionally sampled spectra. Our experiments on battery materials and catalysts demonstrate the method’s effectiveness for both static and dynamic XANES measurements, improving data collection efficiency and enabling better time resolution for tracking chemical changes. This approach advances the degree of automation in XANES experiments, reducing the common errors of under- or over-sampling points near the absorption edge and enabling dynamic experiments that require high temporal resolution or limited measurement time.
Progress and prospects for accelerating materials science with automated and autonomous workflows
Integrating automation with artificial intelligence will enable scientists to spend more time identifying important problems and communicating critical insights, accelerating discovery and development of materials for emerging and future technologies.