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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Cooperative Education

Los Alamos National Laboratory (LANL) is a multidisciplinary national laboratory that conducts research and development in national security, engineering, materials science, computational modeling, and advanced manufacturing. The laboratory develops innovative technologies to address complex scientific and engineering challenges. This project focuses on the development and evaluation of high-performance absorbing structures through computational design, simulation, and engineering analysis. Absorbing structures are used in applications where damage mitigation, structural protection, and material efficiency are critical performance requirements. The increasing demand for lightweight, high-strength, and highly efficient structural systems has created a need for improved design methodologies capable of maximizing absorption while minimizing weight and material usage. The project utilizes advanced engineering software, including 3D CAD software and FEA, to generate and optimize structural concepts. Computational simulations are performed to evaluate structural behavior under loading conditions, while mathematical analyses are conducted using Python-based tools as well as established analytical equations from material and structural mechanics. The project benefits LANL by supporting the development of advanced design methodologies and improving the understanding of material and structural performance. During the internship term, a significant portion of the design development, simulation, and data analysis activities will be completed. Success of the project depends on collaboration among engineering mentors and technical staff members. Work will be conducted at Los Alamos National Laboratory using laboratory computing resources and engineering software.

42 ENGINEERING↗
DOE OSTI2021

Raman Microscopy Analysis of Wyoming CarbonSAFE Pilot Well Thin Sections for Mineralogy and Organic Matter Characterization

Scanning confocal Raman microspectroscopy (RMS) was used to analyze thin sections for inorganic mineral content and for evaluation of organic material (OM). Thin sections were selected from a stratigraphic test well core in the Powder River Basin, Wyoming under the Wyoming CarbonSAFE project. The well was analyzed with a variety of rock and fluid characterization techniques to determine the feasibility of a commercial-scale CO2 storage site. RMS complements other analyses, including traditional petrography, SEM, porosity and permeability, and XRD For this aspect of the study, RMS is especially important in the evaluation of OM in sealing lithologies. At prospective geologic CO2 storage sites, it is imperative to assess the unconventional oil and gas potential of seals to ensure that any future development would not compromise the integrity of the seals. Whole-slide mineralogical surveys were performed on thin sections from various shale and sandstone formations. Surveys were analyzed with Direct Classical Least Squares to identify and quantify minerals. The location and concentration of minerals was color-coded and overlaid on optical images for visualization of the distribution of minerals. Dense hyperspectral Raman mapping of OM was performed on five thin sections. Eleven spectral parameters diagnostic of organic type and thermal maturity were used to train a Partial Least Squares (PLS) calibration against a set of artificially matured samples spanning the pre- to mid-oil window. The PLS was applied to the study set and a post-mature set. Additionally, the PLS was applied to each point in hyperspectral maps for visualization of trends in maturity across sample sets and discrimination of organic matter types within a given map. In inorganic surveys on thin sections, a total of 14 unique inorganic minerals were identified in Raman spectra including quartz, dolomite, calcite, hematite and anhydrite. Shale thin sections tended to be dominated by organic material. OM was often observed mixed with inorganic minerals. Sand- and mudstones were dominated by inorganic minerals. The PLS extrapolated the post-mature set to reflectances >1.2%. The study set ranged from very immature to postmature in the median of map fit-peak parameters. However, point maturity maps indicate that matrix OM in all study samples is immature and that discreet organic particles selected for mapping, which may be inertinites, bias medians towards more-mature. The work demonstrates the capabilities of RMS to perform both whole-slide mineralogy and OM analysis with applications to formation evaluation in oil & gas, carbon sequestration and mining. Here, analysis of sealing formations in the well indicates high levels of immature organic matter that would not be a viable target for future oil production that could compromise the CO2 storage site. The work brings together diverse disciplines from geology and petrography to analytical chemistry, big data and microscopy.

Myers, Grant↗
DOE OSTI2020

Analytic Modeling of a Deep Shielding Problem

Previous generations of scientists would make tremendous efforts to simplify non-tractable problems and generate simpler models that preserved the fundamental physics. This process involved applying assumptions and simplifications to reduce the complexity of the problem until it reached a solvable form. Each assumption and simplification was chosen and applied with the intent to preserve the essential physics of the problem, since, if the core physics of the problem were eliminated, the simplified model served no purpose. Moreover, if done correctly, solutions to the reduced model would serve as useful approximations to the original problem. In a sense, solving the simple models laid the ground-work for and provided insight into the more complex problem. Today, however, the affordability of high performance computing has essentially replaced the process for analyzing complex problems. Rather than "building up" a problem by understanding smaller, simpler models, a user generally relies on powerful computational tools to directly arrive at solutions to complex problems. As computational resources grow, users continue trying to simulate new, more complex, or more detailed problems, resulting in continual stress on both the code and computational resources. When these resources are limited, the user will have to make concessions by simplifying the problem while trying to preserve important details. In the context of the Monte Carlo N-Particle radiation transport simulation tool, simplifications typically come as reductions in geometry, or by using variance reduction techniques. Both approaches can influence the physics of the problem, leading to potentially inaccurate or non-physical results. Errors can also be introduced as a result of faulty input into a computational tool: something as simple as transposing numbers in a tally input can result in incorrect answers. In this paradigm, reduced complexity computational and analytical models still have an important purpose. The explicit form of an analytic solution is arguably the best way to understand the qualitative properties of simple models. In contrast to "building up" a complex problem through understanding simpler problems, results from detailed computational scenarios can be better explained by "building down" the complex model through simple models rooted in the fundamental or essential phenomenology. Simplified analytic and computational models can be used to 1) increase a user's confidence in the computational solution of a complex model, 2) confirm there are no user input errors, and 3) ensure essential assumptions of the simulation tool are preserved. This process of using analytic models to develop a more valuable analysis of simulation results is named the results assessment methodology. The utility of the results assessment methodology and a complimentary sensitivity analysis is exemplified through the analysis of the neutron flux in a dry used fuel storage cask. This application was chosen due to current scientific interest in used nuclear fuel storage.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Analytic Modeling of a Deep Shielding Problem

Previous generations of scientists would make tremendous efforts to simplify non-tractable problems and generate simpler models that preserved the fundamental physics. This process involved applying assumptions and simplifications to reduce the complexity of the problem until it reached a solvable form. Each assumption and simplification was chosen and applied with the intent to preserve the essential physics of the problem, since, if the core physics of the problem were eliminated, the simplified model served no purpose. Moreover, if done correctly, solutions to the reduced model would serve as useful approximations to the original problem. In a sense, solving the simple models laid the ground-work for and provided insight into the more complex problem. Today, however, the affordability of high performance computing has essentially replaced the process for analyzing complex problems. Rather than "building up" a problem by understanding smaller, simpler models, a user generally relies on powerful computational tools to directly arrive at solutions to complex problems. As computational resources grow, users continue trying to simulate new, more complex, or more detailed problems, resulting in continual stress on both the code and computational resources. When these resources are limited, the user will have to make concessions by simplifying the problem while trying to preserve important details. In the context of the Monte Carlo N-Particle radiation transport simulation tool, simplifications typically come as reductions in geometry, or by using variance reduction techniques. Both approaches can influence the physics of the problem, leading to potentially inaccurate or non-physical results. Errors can also be introduced as a result of faulty input into a computational tool: something as simple as transposing numbers in a tally input can result in incorrect answers. In this paradigm, reduced complexity computational and analytical models still have an important purpose. The explicit form of an analytic solution is arguably the best way to understand the qualitative properties of simple models. In contrast to "building up" a complex problem through understanding simpler problems, results from detailed computational scenarios can be better explained by "building down" the complex model through simple models rooted in the fundamental or essential phenomenology. Simplified analytic and computational models can be used to 1) increase a user's confidence in the computational solution of a complex model, 2) con firm there are no user input errors, and 3) ensure essential assumptions of the simulation tool are preserved. This process of using analytic models to develop a more valuable analysis of simulation results is named the results assessment methodology. The utility of the results assessment methodology and a complimentary sensitivity analysis is exempli fied through the analysis of the neutron flux in a dry used fuel storage cask. This application was chosen due to current scientific interest in used nuclear fuel storage.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Multiscale Electrochemomechanics Interaction and Degradation Analytics of Sn Electrodes for Sodium-Ion Batteries

Sodium-ion batteries have emerged as a strong contender among the beyond lithium-ion chemistries due to elemental abundance and the low cost of sodium. Tin (Sn) is a promising alloying electrode with high capacity, redox reversibility, and earth abundance. Tin electrodes, however, undergo a series of intermediate reactions exhibiting multiple voltage plateaus upon sodiation/desodiation. Phase transformations related to incomplete sodiation in tin during cycling, in the presence of a frail solid electrolyte interphase layer, can quickly weaken the structural stability. The structural dynamics and reactivity of the electrode/electrolyte interface, being further dependent on the size and morphology of the active material particle in the presence of different electrolytes, dictate the electrode degradation and survivability during cycling. Here, we paint a comprehensive picture of the underpinnings of the electrochemical and mechanics coupling and electrode/electrolyte interfacial interactions in alloying Sn electrodes. We elicit the fundamental role of electrode/electrolyte complexations in the Sn electrode structure-property-performance relationship based on multimodal analytics, including electrochemical, microscopy, and tomography analyses.

25 ENERGY STORAGE↗
DOE OSTI2023

Robust scalable initialization for Bayesian variational inference with multi-modal Laplace approximations

Predictive modeling typically relies on Bayesian model calibration to provide uncertainty quantification. Variational inference utilizing fully independent (“mean-field”) Gaussian distributions are often used as approximate probability density functions. This simplification is attractive since the number of variational parameters grows only linearly with the number of unknown model parameters. However, the resulting diagonal covariance structure and unimodal behavior can be too restrictive to provide useful approximations of intractable Bayesian posteriors that exhibit highly non-Gaussian behavior, including multimodality. High-fidelity surrogate posteriors for these problems can be obtained by considering the family of Gaussian mixtures. Gaussian mixtures are capable of capturing multiple modes and approximating any distribution to an arbitrary degree of accuracy, while maintaining some analytical tractability. Unfortunately, variational inference using Gaussian mixtures with full-covariance structures suffers from a quadratic growth in variational parameters with the number of model parameters. The existence of multiple local minima due to strong nonconvex trends in the loss functions often associated with variational inference present additional complications, These challenges motivate the need for robust initialization procedures to improve the performance and computational scalability of variational inference with mixture models. In this work, we propose a method for constructing an initial Gaussian mixture model approximation that can be used to warm-start the iterative solvers for variational inference. The procedure begins with a global optimization stage in model parameter space. In this step, local gradient-based optimization, globalized through multistart, is used to determine a set of local maxima, which we take to approximate the mixture component centers. Around each mode, a local Gaussian approximation is constructed via the Laplace approximation. Finally, the mixture weights are determined through constrained least squares regression. The robustness and scalability of the proposed methodology is demonstrated through application to an ensemble of synthetic tests using high-dimensional, multimodal probability density functions. Here, the practical aspects of the approach are demonstrated with inversion problems in structural dynamics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Deep Convolutional Neural Networks Exploit High-Spatial- and -Temporal-Resolution Aerial Imagery to Phenotype Key Traits in Miscanthus

Miscanthus is one of the most promising perennial crops for bioenergy production, with high yield potential and a low environmental footprint. The increasing interest in this crop requires accelerated selection and the development of new screening techniques. New analytical methods that are more accurate and less labor-intensive are needed to better characterize the effects of genetics and the environment on key traits under field conditions. We used persistent multispectral and photogrammetric UAV time-series imagery collected 10 times over the season, together with ground-truth data for thousands of Miscanthus genotypes, to determine the flowering time, culm length, and biomass yield traits. We compared the performance of convolutional neural network (CNN) architectures that used image data from single dates (2D-spatial) versus the integration of multiple dates by 3D-spatiotemporal architectures. The ability of UAV-based remote sensing to rapidly and non-destructively assess large-scale genetic variation in flowering time, height, and biomass production was improved through the use of 3D-spatiotemporal CNN architectures versus 2D-spatial CNN architectures. The performance gains of the best 3D-spatiotemporal analyses compared to the best 2D-spatial architectures manifested in up to 23% improvements in R2, 17% reductions in RMSE, and 20% reductions in MAE. The integration of photogrammetric and spectral features with 3D architectures was crucial to the improved assessment of all traits. In conclusion, our findings demonstrate that the integration of high-spatiotemporal-resolution UAV imagery with 3D-CNNs enables more accurate monitoring of the dynamics of key phenological and yield-related crop traits. This is especially valuable in highly productive, perennial grass crops such as Miscanthus, where in-field phenotyping is especially challenging and traditionally limits the rate of crop improvement through breeding.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Manifold Learning-Based Polynomial Chaos Expansions for High-Dimensional Surrogate Models

In this work we introduce a manifold learning-based method for uncertainty quantification (UQ) in systems describing complex spatiotemporal processes. Our first objective is to identify the embedding of a set of high-dimensional data representing quantities of interest of the computational or analytical model. For this purpose, we employ Grassmannian diffusion maps, a two-step nonlinear dimension reduction technique which allows us to reduce the dimensionality of the data and identify meaningful geometric descriptions in a parsimonious and inexpensive manner. Polynomial chaos expansion is then used to construct a mapping between the stochastic input parameters and the diffusion coordinates of the reduced space. An adaptive clustering technique is proposed to identify an optimal number of clusters of points in the latent space. The similarity of points allows us to construct a number of geometric harmonic emulators which are finally utilized as a set of inexpensive pretrained models to perform an inverse map of realizations of latent features to the ambient space and thus perform accurate out-of-sample predictions. Thus, the proposed method acts as an encoder-decoder system which is able to automatically handle very high-dimensional data while simultaneously operating successfully in the small-data regime. The method is demonstrated on two benchmark problems and on a system of advection-diffusion-reaction equations which model a first-order chemical reaction between two species. In all test cases, the proposed method is able to achieve highly accurate approximations which ultimately lead to the significant acceleration of UQ tasks.

42 ENGINEERING↗
DOE OSTI2022

Understanding the Role of Order in Y‐Series Non‐Fullerene Solar Cells to Realize High Open‐Circuit Voltages

Abstract Non‐fullerene acceptors (NFAs) as used in state‐of‐the‐art organic solar cells feature highly crystalline layers that go along with low energetic disorder. Here, the crucial role of energetic disorder in blends of the donor polymer PM6 with two Y‐series NFAs, Y6, and N4 is studied. By performing temperature‐dependent charge transport and recombination studies, a consistent picture of the shape of the density of state distributions for free charges in the two blends is developed, allowing an analytical description of the dependence of the open‐circuit voltage V OC on temperature and illumination intensity. Disorder is found to influence the value of the V OC at room temperature, but also its progression with temperature. Here, the PM6:Y6 blend benefits substantially from its narrower state distributions. The analysis also shows that the energy of the equilibrated free charge population is well below the energy of the NFA singlet excitons for both blends and possibly below the energy of the populated charge transfer manifold, indicating a down‐hill driving force for free charge formation. It is concluded that energetic disorder of charge‐separated states has to be considered in the analysis of the photovoltaic properties, even for the more ordered PM6:Y6 blend.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Self-supported MoO 2 /MoS 2 nano-sheets embedded in a carbon cloth as a binder-free substrate for high-energy lithium–sulfur batteries

As one of the most prospective candidates for next-generation rechargeable batteries, lithium-sulfur (Li-S) batteries currently still encounter great challenges associated with the low conductivity, severe shuttle effects, and sluggish redox kinetics. Herein, a self-supported sulfur host is fabricated by an in-situ growth of MoO 2 /MoS 2 nano-sheets on a carbon cloth (CC). First, carbon cloth serves as a good template for the morphology-controlled synthesis of nanostructured materials, which not only alleviates the agglomeration of MoO2/MoS2, but also enhances the flexibility and mechanical strengthen of the hybrid architecture as a free-standing host. Second, the polysulfide-trapping ability can be greatly enhanced by both physical and chemical adsorption from the MoO 2 /MoS 2 -anchored carbon cloth. Moreover, the partially sulfurized MoO 2 /MoS 2 nano-sheets integrate the benefits of conductive MoO 2 and sulfiphilic MoS 2 , thus facilitating fast charge transfer and redox kinetics of polysulfide conversion. As a result, these attributes enable the host to hold a high sulfur loading (up to 7.6 mg cm -2 ), which exceeds most of the reported carbon cloth-related cathode work in the literature. Furthermore, the Li-S cells can achieve a high peak capacity of 1350 mA h g -1 , excellent rate capability (C/20 - 2C rate), impressive areal capacity (up to 6.3 mA h cm -2 ), and a high capacity retention of 85% after 100 cycles.

25 ENERGY STORAGE↗
DOE OSTI2024

Data Analytics for Catalysis Predictions: Are We Ready Yet?

Catalysis informatics has received tremendous attention in recent years as a tool to design catalysts and discover unique descriptors that capture the relationships between chemical properties and catalytic performance. One of the stop-gaps in understanding catalytic effects, which is often ignored and limits the deployment of data science tools, relates to the lack of uniform data. The catalytic cleavage of C–X (X= H, C, N, and O) bonds is relevant to many fundamental catalytic processes. In this Perspective, we performed data analytics on four groups of C–X cleavage reactions that are common in production, upcycling, or reactive separation: the C–C cleavage in cyclopropyl alcohol, the C–H cleavage in hydroacylation reactions, the C–O cleavage in β-O-4 linkages, and the C–N cleavage in amides, using experimental data collected from the literature to understand their underlying correlations. Experimental variables of high impact are identified for each reaction by dimensionality reduction methods. We highlight the urgent need for experimental data sets that include full details on the reaction conditions, such as reagent concentration, reaction temperature, or time in machine-readable forms. We discuss the potential improvement of the data of these reactions and promising approaches such as autonomous experiments to fill the gaps in unbiased experimental data. Finally, we also address the early stage consideration of separation aspects in the experimental design of efficient catalytic systems for these fundamental examples of chemical reactivity.

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

Uranium Bed Design Parameters for Tritium Plants Supporting Fusion Reactors

Uranium hydride is commonly used to store hydrogen or its isotopes in a solid state. The Self-Assaying Tritium Accountancy and Containment Unit for ITER (STACI) is a 5.2-kg bed of depleted uranium (dU) capable of holding up to 33 mol of hydrogen or its isotopes. Here we summarize the data analysis of past experimental campaigns with STACI, with the aim of describing the kinetics and thermodynamics of the hydriding process. Computed tomography imaging was performed on STACI both before and after its experimental campaign, and a high degree of swelling was observed in the dU. Literature on studies in regard to the swelling of large (multikilogram) quantities of uranium hydride for storage applications was not identified during this study. Data from the experimental campaign, as well as data on the formation reaction, are presented. The authors hope to create an analytical model of STACI based on these data.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

Performance Evaluation of AG-1 FC HEPA Filters and Medium in Nuclear Complex Facilities - 20434

High Efficiency Particulate Air (HEPA) filters are credited as the final barrier against the release of radioactive aerosol contamination in nearly every operating U. S. Department of Energy (DOE) and National Nuclear Security Agency (NNSA) nuclear facility. The Institute for Clean Energy Technology (ICET) at Mississippi State University maintains a research program that studies various aspects of these components of containment systems and seeks to answer key questions from across the industry. This programmatic overview will include results from recent and ongoing studies, including a study on filter design and performance envelope, degradation due to aging or fatigue, fire event impact on HEPA filters, and accelerated aging of medium and component parts. ICET has completed a study helping to define the threshold for combined elevated temperature and relative humidity resistance of separator style and separator-less style HEPA filters that are used in DOE complexes. The study provides experimental data that considers elevated temperature, elevated relative humidity (RH), and target differential pressure (dP) in an attempt to gain a better understanding or more comprehensive insight into the operating envelope of different configurations of ASME (American Society for Mechanical Engineers) AG-1 Section FC filters. Test variables including air temperature ranges of 48.88 deg. C, 54.44 deg. C, or 60 deg. C (120, 130, or 140 deg. F); air RH ranges including 60-70%, 80%, or 90+% with initial filter dP of either 497.68 Pa, 746.52 Pa, or 995.36 Pa (2 inches water column (in. w. c.), 3 in. w. c., or 4 in. w. c.) included for full bracketing of each set of conditions. A study examining the effects of aging and fatigue on nuclear grade HEPA filters and medium elucidates the physical properties of media along with testing of new and aged ASME AG-1 HEPA filters. In addition to performing autopsies of the tested, aged filters to help better understand the service life of their performance, newly designed accelerated aging chambers allow new medium to undergo accelerated aging and exposure treatments. Evaluation of degradation in physical properties and functionality requires a high population of aged filters of different ages, designs, manufacturers, and operational histories. The limited availability of this population and the impropriety of looking solely at formulations and components from past years requires experimental design that will provide foresight into future filter performance. This undertaking therefore involves not only properties analysis of aged medium from different operational histories, ages, designs, and manufacturers, but also prescience in analytical goals via accelerated aging studies of newly manufactured media. A more complete understanding of the mechanisms of degradation from past formulations as well as current and future formulations is possible. Accelerated Aging of newly manufactured media utilizes exposure treatments based on the Arrhenius equation to artificially age medium and therefore glimpse into the future. Evaluation of current media properties This testing allows for the comparison of performance and durability of new filters under upset or design basis conditions with aged filters that were in service under ambient conditions and other aged filters retained in storage. Susceptibility of HEPA filters to the effects of combustion byproducts, heat, and water is also being studied in an attempt to gain understanding on the prevention of filter failures during fire events in nuclear facilities. ICET seeks to determine the effect of filter performance due to smoke loading using characterized smoke from variable fuel compositions, geometries, and loads; the smoke capacity of various filters with different burning conditions, heat release rates and transport, as well as mass transport from the air stream will be studied in low flow containment systems. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Simultaneous Control of Flexibility and Rigidity in Pore-Space-Partitioned Metal–Organic Frameworks

Flexi-MOFs are typically limited to low-connected (< 9) frameworks. Here we report a platform-wide approach capable of creating a family of high-connected materials (collectively called CPM-220) that integrate exceptional framework flexibility with high rigidity. We show that the multi-module nature of the pore-space-partitioned pacs (partitioned acs net) platform allows us to introduce flexibility as well as to simultaneously impose high rigidity in a tunable module-specific fashion. The inter-modular synergy has remarkable macro-morphological and sub-nanometer structural impacts. A prominent manifestation at both length scales is the retention of X-ray-quality single crystallinity despite huge hexagonal c-axial contraction (≈ 30%) and harsh sample treatment such as degassing and sorption cycles. CPM-220 sets multiple precedents and benchmarks on the pacs platform in both structural and sorption properties. Furthermore, they possess exceptionally high benzene/cyclohexane selectivity, unusual C 3 H 6 and C 3 H 8 isotherms, and promising separation performance for small gas molecules such as C 2 H 2 /CO 2 .

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

A critical comparison of CH∙∙∙π versus π∙∙∙π interactions in the benzene dimer: obtaining benchmarks at the CCSD(T) level and assessing the accuracy of lower scaling methods

In this study, we have established CCSD(T)/CBS (Complete Basis Set) limits for 3 stationary points on the benzene dimer potential energy surface, corresponding to the π∙∙∙π (parallel displaced or PD(C 2h ), minimum) and CH∙∙∙π (T-shaped or T(C 2v ), transition state) and tilted T-shaped (or TT(Cs), minimum) bonding scenarios considering both the structure and binding energy. The CCSD(T)/CBS binding energies are -2.65 ± 0.02 (PD), -2.74 ± 0.03 (T), and -2.83 ± 0.01 kcal mol -1 (TT). To this end, the CH∙∙∙π is ~0.2 kcal mol -1 stronger than the π∙∙∙π interaction, whereas the tilting of the CH donating benzene molecule with respect to the other benzene is worth 0.1 kcal mol -1 . As previously discussed in the literature, the MP2 level of theory does not provide a close match for either the energy or structure, yet the SCS-MP2 yields structures in excellent agreement with respect to the CCSD(T) result. It is found that the SCS-MI-MP2 also gives optimized structures very close to SCS-MP2 (within ~0.01 Å of the benchmark). Despite the closer match in structure, the spin-biased MP2 methods (SCS-, SCS-MI-, and SOS-MP2) incorrectly predict the relative stabilities of the isomers. That said, none of the spin biased MP2 methods offers a good compromise between energy and structure for the systems examined. Finally, the CCSD(T)/CBS benchmarks were used to assess the performance of 13 DFT functionals selected from different rungs of Jacob's ladder. Several functionals such as TPSS-D3, B3LYP-D3, B97-D, B97-D3, and B2PLYP-D3 provided a good description of the binding energies for both CH∙∙∙π and π∙∙∙π interactions, yielding values within 6% of the CCSD(T)/CBS benchmark values. Unlike the MP2 methods, these functionals correctly predict the relative stability of the PD(C 2h ) and T(C 2v ) dimers. Further, we find that there is no systematic improvement as Jacob's ladder is ascended (increased complexity of functional). The best functionals that result in a good compromise between structure and energy accuracy are B97-D3 and B2PLYP-D3 for both the CH∙∙∙π and π∙∙∙π interaction. Despite the impressive performance of these functionals, a challenge that remains is ensuring the transferability of these density functionals in accurately describing the interaction between dimers of larger aromatic molecules, the latter requiring high-level benchmarks for these systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Intro to HPC Bootcamp: Engaging New Communities Through Energy Justice Projects

The U.S. Department of Energy (DOE) is a long-standing leader in research and development of high-performance computing (HPC) in the pursuit of science. However, we face daunting challenges in fostering a robust and diverse HPC workforce. Basic HPC is not typically taught at early stages of students' academic careers, and the capacity and knowledge of HPC at many institutions are limited. Even so, such topics are prerequisites for advanced training programs, internships, graduate school, and ultimately for careers in HPC. To help address this challenge, as part of the DOE Exascale Computing Project's Broadening Participation Initiative, we recently launched the Introduction to HPC Training and Workforce Pipeline Program to provide accessible introductory material on HPC, scalable AI, and analytics. We describe the Intro to HPC Bootcamp, an immersive program designed to engage students from underrepresented groups as they learn foundational HPC skills. Here, the program takes a novel approach to HPC training by turning the traditional curriculum upside down. Instead of focusing on technology and its applications, the bootcamp focuses on energy justice to motivate the training of HPC skills through project-based pedagogy and real-life science stories. Additionally, the bootcamp prepares students for internships and future careers at DOE labs. The first bootcamp, hosted by the advanced computing facilities at Argonne, Lawrence Berkeley, and Oak Ridge National Labs and organized by Sustainable Horizons Institute, took place in August 2023.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2024

Overview of Quantum Sensing Materials and Techniques for Energy Sector Applications

The energy sector has become increasingly dependent upon highly sensitive sensing devices for a wide range of applications. Variables such as temperature, pH, electromagnetic fields, ions, and pressure must be measured with high precision, often in harsh conditions (e.g. highly corrosive environments due to high temperature, pressure and humidity). These sensors are deployed in infrastructure such as transformers, pipelines, mines, nuclear power plants, and other areas to ensure safe operating conditions and uninterrupted, optimized service. Moreover, new opportunities for sensors have emerged due to the expansion of smart grids/meters, driverless vehicles, and the discovery of new oil, gas, and critical mineral deposits. The continued maturation of quantum sensing technologies offers exciting opportunities for quantum-enhanced measurements within the energy sector that may provide significant improvements in sensitivity beyond the classical limit. Here, an overview of established and emerging quantum materials for sensing applications will be provided, from trapped ions to color centers in diamond and silicon carbide. Associated quantum sensing methods for these materials will be discussed, with an emphasis on platforms that are nearing commercial viability. Specific application opportunities within the energy sector for quantum sensors will then be analyzed, including oil/gas discovery, greenhouse gas emission monitoring, pH and ion sensing, current measurement in electric vehicle batteries, and quantum-enhanced spectroscopy. Remaining barriers, such as quantum sensor platform miniaturization and ruggedization, will also be analyzed. A specific project at the National Energy Technology Laboratory involving the functionalization of qubits using metal-organic frameworks for enhanced quantum sensing will also be highlighted. Here, nitrogen vacancy centers (NV) in nanodiamonds, a commercially available qubit with long coherence times and utilizable quantum properties at room temperature, are encapsulated in a controlled way using the metal-organic framework ZIF-8. The ZIF-8 provides a well-defined, porous scaffold that can be controlled to promote the selective uptake of specific target analytes, such as gas molecules or ions. The quantum sensing performance of this composite material is either preserved or enhanced following ZIF-8 encapsulation; the optically detected magnetic resonance response of the NV nanodiamonds with and without the ZIF-8 coating are identical, while the ZIF-8 coating increases the longitudinal spin relaxation lifetime (T1) of the NV centers, an important parameter for spin relaxometry-based quantum sensing experiments. Taken together, these results demonstrate the importance of qubit functionalization as a crucial step for rationally designing high performance quantum sensors.

Crawford, Scott↗