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At least 649 records · Page 36

Nuclear spin dependence of the reaction of H{sub 3}{sup +} with H{sub 2}. I. Kinetics and modeling

The chemical reaction H{sub 3}{sup +}+ H{sub 2}{yields} H{sub 2}+H{sub 3}{sup +} is the simplest bimolecular reaction involving a polyatomic, yet is complex enough that exact quantum mechanical calculations to adequately model its dynamics are still unfeasible. In particular, the branching fractions for the ''identity,''''proton hop,'' and ''hydrogen exchange'' reaction pathways are unknown, and to date, experimental measurements of this process have been limited. In this work, the nuclear-spin-dependent steady-state kinetics of the H{sub 3}{sup +}+ H{sub 2} reaction is examined in detail, and employed to generate models of the ortho:para ratio of H{sub 3}{sup +} formed in plasmas of varying ortho:para H{sub 2} ratios. One model is based entirely on nuclear spin statistics, and is appropriate for temperatures high enough to populate a large number of H{sub 3}{sup +} rotational states. Efforts are made to include the influence of three-body collisions in this model by deriving nuclear spin product branching fractions for the H{sub 5}{sup +}+ H{sub 2} reaction. Another model, based on rate coefficients calculated using a microcanonical statistical approach, is appropriate for lower-temperature plasmas in which energetic considerations begin to compete with the nuclear spin branching fractions. These models serve as a theoretical framework for interpreting the results of laboratory studies on the reaction of H{sub 3}{sup +} with H{sub 2}.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Machine-learning interatomic potentials for interfaces in all-solid-state batteries: Perspectives on training data, model selection, and validation

Interfaces play a pivotal role in dictating the performance and reliability of all-solid-state batteries (ASSBs), where complex electro-chemo-mechanical phenomena at grain boundaries (GBs) and interfaces can lead to degradation and failure. Traditional atomistic simulation methods, such as first-principles calculations and classical molecular dynamics, face limitations in modeling these interfaces due to either high computational cost or insufficient transferability to the diverse atomic environments evolving at interfaces. Machine-learning interatomic potentials (MLIPs) have emerged as a transformative approach, enabling large-scale, high-accuracy simulations of disordered and chemically complex systems by leveraging the predictability of machine learning models trained on first-principles data. Recent applications of MLIPs have demonstrated their ability to capture intricate behaviors at ASSB interfaces, including ion transport, interfacial evolution, and degradation mechanisms, with accuracy and efficiency unattainable by conventional methods. This prospective paper presents comprehensive analysis and practical guidance for MLIP development for GBs and interfaces in ASSBs, with a focus on three key pillars: data generation, model selection, and validation. Here, we review the current state of MLIP applications for GBs and interfaces in both general and ASSB-specific materials, highlighting best practices and challenges in constructing diverse and representative datasets, choosing appropriate machine learning architectures, and rigorously validating model performance. We also discuss emerging strategies and opportunities for improved reliability and efficiency of MLIPs to simulate realistic interfaces in ASSBs.

Energy - Storage↗

Trust Your Gut: Comparing Human and Machine Inference from Noisy Visualizations

People commonly utilize visualizations not only to examine a given dataset, but also to draw generalizable conclusions about the underlying models or phenomena. Prior research has compared human visual inference to that of an optimal Bayesian agent, with deviations from rational analysis viewed as problematic. However, human reliance on non-normative heuristics may prove advantageous in certain circumstances. We investigate scenarios where human intuition might surpass idealized statistical rationality. In two experiments, we examine individuals’ accuracy in characterizing the parameters of known data-generating models from bivariate visualizations. Our findings indicate that, although participants generally exhibited lower accuracy compared to statistical models, they frequently outperformed Bayesian agents, particularly when faced with extreme samples. Participants appeared to rely on their internal models to filter out noisy visualizations, thus improving their resilience against spurious data. However, participants displayed overconfidence and struggled with uncertainty estimation. They also exhibited higher variance than statistical machines. Our findings suggest that analyst gut reactions to visualizations may provide an advantage, even when departing from rationality. These results carry implications for designing visual analytics tools, offering new perspectives on how to integrate statistical models and analyst intuition for improved inference and decision-making. The data and materials for this paper are available at https://osf.io/qmfv6

human-machine collaboration↗

Estimation of Melting-Layer Cooling Rate from Dual-Polarization Radar: Spectral Bin Model Simulations

Diabatic cooling from hydrometeor phase changes in the stratiform melting layer is of great interest to both operational forecasters and modelers for its societal and dynamical consequences. Attempts to estimate the melting-layer cooling rate typically rely on either the budgeting of hydrometeor content estimated from reflectivity Z or model-generated lookup tables scaled by the magnitude of Z in the bright band. Recent advances have been made in developing methods to observe the unique polarimetric characteristics of melting snow and the additional microphysical information they may contain. However, to date no work has looked at the thermodynamic information available from the polarimetric radar brightband signature. In this study, a one-dimensional spectral bin model of melting snow and a coupled polarimetric operator are used to study the relation between the polarimetric radar bright band and the melting-layer cooling rate. Simulations using a fixed particle size distribution (PSD) and variable environmental conditions show that the height and thickness of the bright band and the maximum brightband Z and specific differential phase shift [Formula: see text] are all sensitive to the ambient environment, while the differential reflectivity [Formula: see text] is relatively insensitive. Additional simulations of 2700 PSDs based on in situ observations above the melting layer indicate that the maximum Z, [Formula: see text], and [Formula: see text] within the melting layer are poorly correlated with the maximum cooling rate while [Formula: see text] is strongly correlated. Finally, model simulations suggest that, in addition to riming, concurrent changes in aggregation and precipitation intensity and the associated cooling may plausibly cause observed sagging brightband signatures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Modeling Spatial Asymmetries in Teleconnected Extreme Temperatures

Abstract Combining strengths from deep learning and extreme value theory can help describe complex relationships between variables where extreme events have significant impacts (e.g., environmental or financial applications). Neural networks learn complicated nonlinear relationships from large datasets under limited parametric assumptions. By definition, the number of occurrences of extreme events is small, which limits the ability of the data-hungry, nonparametric neural network to describe rare events. Inspired by recent extreme cold winter weather events in North America caused by atmospheric blocking, we examine several probabilistic generative models for the entire multivariate probability distribution of daily boreal winter surface air temperature. We propose metrics to measure spatial asymmetries, such as long-range anticorrelated patterns that commonly appear in temperature fields during blocking events. Compared to vine copulas, the statistical standard for multivariate copula modeling, deep learning methods show improved ability to reproduce complicated asymmetries in the spatial distribution of ERA5 temperature reanalysis, including the spatial extent of in-sample extreme events.

Krock, Mitchell L.↗

Multiscale modeling of polyisoprene on graphite

The local dynamics and the conformational properties of polyisoprene next to a smooth graphite surface constructed by graphene layers are studied by a multiscale methodology. First, fully atomistic molecular dynamics simulations of oligomers next to the surface are performed. Subsequently, Monte Carlo simulations of a systematically derived coarse-grained model generate numerous uncorrelated structures for polymer systems. A new reverse backmapping strategy is presented that reintroduces atomistic detail. Finally, multiple extensive fully atomistic simulations with large systems of long macromolecules are employed to examine local dynamics in proximity to graphite. Polyisoprene repeat units arrange close to a parallel configuration with chains exhibiting a distribution of contact lengths. Efficient Monte Carlo algorithms with the coarse-grain model are capable of sampling these distributions for any molecular weight in quantitative agreement with predictions from atomistic models. Furthermore, molecular dynamics simulations with well-equilibrated systems at all length-scales support an increased dynamic heterogeneity that is emerging from both intermolecular interactions with the flat surface and intramolecular cooperativity. This study provides a detailed comprehensive picture of polyisoprene on a flat surface and consists of an effort to characterize such systems in atomistic detail.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Optical bandgap of single- and multi-layered amorphous germanium ultra-thin films

Accurate optical methods are required to determine the energy bandgap of amorphous semiconductors and elucidate the role of quantum confinement in nanometer-scale, ultra-thin absorbing layers. Here, we provide a critical comparison between well-established methods that are generally employed to determine the optical bandgap of thin-film amorphous semiconductors, starting from normal-incidence reflectance and transmittance measurements. First, we demonstrate that a more accurate estimate of the optical bandgap can be achieved by using a multiple-reflection interference model. We show that this model generates more reliable results compared to the widely accepted single-pass absorption method. Second, we compare two most representative methods (Tauc and Cody plots) that are extensively used to determine the optical bandgap of thin-film amorphous semiconductors starting from the extracted absorption coefficient. Analysis of the experimental absorption data acquired for ultra-thin amorphous germanium (a-Ge) layers demonstrates that the Cody model is able to provide a less ambiguous energy bandgap value. Finally, we apply our proposed method to experimentally determine the optical bandgap of a-Ge/SiO{sub 2} superlattices with single and multiple a-Ge layers down to 2 nm thickness.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

AGR-1, AGR-2 and AGR-3/4 Release-to-Birth Ratio Data Analysis

A series of Advanced Gas Reactor (AGR) irradiation tests is being conducted in the Advanced Test Reactor at Idaho National Laboratory in support of development and qualification of tristructural isotropic (TRISO) low enriched fuel used in the High Temperature Gas-cooled Reactor (HTGR). Each AGR test consists of multiple independently controlled and monitored capsules containing fuel compacts placed in a graphite cylinder shrouded by a steel shell. These capsules are instrumented with thermocouples embedded in the graphite enabling temperature control. AGR configuration and irradiation conditions are based on prismatic HTGR technology distinguished primarily by the use of helium coolant, a low-power-density ceramic core capable of withstanding very high temperatures, and TRISO coated particle fuel. Thus, these tests provide valuable irradiation performance data to support fuel process development, qualify fuel for normal operating conditions, and support development and validation of fuel performance and fission product transport models and codes. The release-rate-to-birth-rate ratio (R/B) for each of fission product isotopes (i.e., krypton and xenon) is calculated from release rates in the sweep gas flow measured by the germanium detectors used in the AGR Fission Product Monitoring (FPM) System installed downstream from each irradiated capsule. Birth rates are calculated based on the fission power in the experiment and fission product generation models. Thus, this R/B is a measure of the ability of fuel kernel, particle coating layers, and compact matrix to retain fission gas atoms preventing their release into the sweep gas flow, especially in the event of particle coating failures that occurred during AGR-2 and AGR-3/4 irradiations. The major factors that govern gaseous radioactive decay, diffusion, and release processes are found to be material diffusion coefficient, temperature, and isotopic decay constant. For each of all AGR capsules, ABAQUS-based three-dimensional finite-element thermal models are created to predict daily averages of fuel compact temperatures for the entire irradiation period, which are used in establishing the R/B correlation with temperature and decay constant. This correlation can be used by reactor designers to estimate fission gas release from postulated failed fuel particles in HTGR cores, which is the key safety factor for fuel performance assessment.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

AGR-2 and AGR-3/4 Release-to-Birth Ratio Data Analysis

A series of Advanced Gas Reactor (AGR) irradiation tests is being conducted in the Advanced Test Reactor at Idaho National Laboratory in support of development and qualification of tristructural isotropic (TRISO) low enriched fuel used in the High Temperature Gas-cooled Reactor (HTGR). Each AGR test consists of multiple independently controlled and monitored capsules containing fuel compacts placed in a graphite cylinder shrouded by a steel shell. These capsules are instrumented with thermocouples embedded in the graphite enabling temperature control. AGR configuration and irradiation conditions are based on prismatic HTGR technology distinguished primarily by the use of helium coolant, a low-power-density ceramic core capable of withstanding very high temperatures, and TRISO coated particle fuel. Thus, these tests provide valuable irradiation performance data to support fuel process development, qualify fuel for normal operating conditions, and support development and validation of fuel performance and fission product transport models and codes. The release-rate-to-birth-rate ratio (R/B) for each of fission product isotopes (i.e., krypton and xenon) is calculated from release rates in the sweep gas flow measured by the germanium detectors used in the AGR Fission Product Monitoring (FPM) System installed downstream from each irradiated capsule. Birth rates are calculated based on the fission power in the experiment and fission product generation models. Thus, this R/B is a measure of the ability of fuel kernel, particle coating layers, and compact matrix to retain fission gas atoms preventing their release into the sweep gas flow, especially in the event of particle coating failures that occurred during AGR-2 and AGR-3/4 irradiations. The major factors that govern gaseous radioactive decay, diffusion, and release processes are found to be material diffusion coefficient, temperature, and isotopic decay constant. For each of all AGR capsules, ABAQUS-based three-dimensional finite-element thermal models are created to predict daily averages of fuel compact temperatures for the entire irradiation period, which are used in establishing the R/B correlation with temperature and decay constant. This correlation can be used by reactor designers to estimate fission gas release from postulated failed fuel particles in HTGR cores, which is the key safety factor for fuel performance assessment.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Chromium-doped uranium dioxide fuels: A review

UO 2 doped with parts per million CR 2 O 3 powder is considered a potential near term accident tolerant fuel candidate. Here, the results of decades of industry and academic research into Cr-doped UO 2 are analyzed and their shortcomings are critiqued. Focusing on the incorporation mechanisms of Cr into the fuel matrix, we explore a mechanistic understanding of the characteristic properties of Cr-doped UO 2 , notably, enhanced fission gas retention attributed to enlarged grain sizes following sintering, along with marginal improvements in the thermophysical properties. The findings of recent X-ray Adsorption Near Edge Spectroscopy studies were compared and put into conversation with historic data regarding the incorporation of Cr in UO 2 . On the basis of defect mechanisms, the case is made for the substitutional incorporation of Cr governing the lattice solubility but not the enhanced U diffusivity. Instead, Cr/CR 2 O 3 redox chemistry in a well-defined oxygen potential explains the differences in the U diffusivity and O/M ratio. The primary mechanism of doping enhanced grain growth is found to be liquid assisted sintering due to a CRO (1) eutectic phase at the grain boundaries. The role of inhomogeneities in Cr concentration in UO 2 at various length scales across the materials microstructure is highlighted and connected to promising experimental and modeling work to fill in the gaps in the current understanding of Cr-doped UO 2 . The review considers both the open scientific questions and engineering applications to illustrate the deep connections between the practice and theory in the design of accident tolerant nuclear fuels. In conclusion, the review ends with an outline of future works that combine meticulous irradiation studies and high resolution experiments with next generation modeling and simulations techniques empowered by machine learning advances to accelerate the fabrication and adoption of Cr-doped UO 2 light water reactors.

Cleveland, Mack Wesley [Massachusetts Inst. of Tec↗

Prediction of Carbon Dioxide Adsorption via Deep Learning

Abstract Porous carbons with different textural properties exhibit great differences in CO 2 adsorption capacity. It is generally known that narrow micropores contribute to higher CO 2 adsorption capacity. However, it is still unclear what role each variable in the textural properties plays in CO 2 adsorption. Herein, a deep neural network is trained as a generative model to direct the relationship between CO 2 adsorption of porous carbons and corresponding textural properties. The trained neural network is further employed as an implicit model to estimate its ability to predict the CO 2 adsorption capacity of unknown porous carbons. Interestingly, the practical CO 2 adsorption amounts are in good agreement with predicted values using surface area, micropore and mesopore volumes as the input values simultaneously. This unprecedented deep learning neural network (DNN) approach, a type of machine learning algorithm, exhibits great potential to predict gas adsorption and guide the development of next‐generation carbons.

Zhang, Zihao↗

Assessing Temporal Effect of Economic Activity on Freight Volumes with Two-Period Cross-Sectional Data

The most comprehensive publicly available freight databases are the Commodity Flow Survey (CFS) and the FHWA s Freight Analysis Framework (FAF). These two sources contain dollar value and weight of freight movements at high geographic levels, such as state or metropolitan areas. Due to the difficulty in obtaining freight data at lower geographies various practitioners and researchers have been suggesting to estimate freight models based on aggregate data. Following these recent practices, a methodology to estimate a nationwide production and attraction models for U.S. domestic trade of goods is presented. To this end, a CFS s data set provided by U.S. Census Bureau and composed of two-nonconsecutive year period (2002 and 2007) of movements of goods between U.S. states for 27 industry sectors was used. The state payroll by industry sector, obtained from the County Business Patterns of the U.S. Census, was the variable used to estimate freight generation models. The main objective of this paper is to analyze the temporal stability and predictability of the proposed aggregate models. The results indicate that the payroll alone explains a significant portion of the freight production and attraction at the state level. However, such simplification in the model process did not result in reasonable predictions of freight for a future year horizon. It is recommended that time-dependent factors (e.g. variables related to changes industry productivity) affecting freight demand should be considered in the modeling process.

Oliveira Neto, Francisco Moraes↗

3D reconstruction of carbon nanotube networks from neutron scattering experiments

Structure reconstruction from statistical descriptors, such as scattering data obtained using x-rays or neutrons, is essential in understanding various properties of nanocomposites. Scattering based reconstruction can provide a realistic model, over various length scales, that can be used for numerical simulations. In this study, 3D reconstruction of a highly loaded carbon nanotube (CNT)-conducting polymer system based on small and ultra-small angle neutron scattering (SANS and USANS, respectively) data was performed. These light-weight and flexible materials have recently shown great promise for high-performance thermoelectric energy conversion, and their further improvement requires a thorough understanding of their structure-property relationships. The first step in achieving such understanding is to generate models that contain the hierarchy of CNT networks over nano and micron scales. The studied system is a single walled carbon nanotube (SWCNT)/poly (3,4-ethylenedioxythiophene): poly (styrene sulfonate) (PEDOT: PSS). SANS and USANS patterns of the different samples containing 10, 30, and 50 wt% SWCNTs were measured. These curves were then utilized to calculate statistical two-point correlation functions of the nanostructure. These functions along with the geometrical information extracted from SANS data and scanning electron microscopy images were used to reconstruct a representative volume element (RVE) nanostructure. Generated RVEs can be used for simulations of various mechanical and physical properties. This work, therefore, introduces a framework for the reconstruction of 3D RVEs of high volume faction nanocomposites containing high aspect ratio fillers from scattering experiments.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

Prediction of Carbon Dioxide Adsorption via Deep Learning

Porous carbons with different textural properties exhibit great differences in CO 2 adsorption capacity. It is generally known that narrow micropores contribute to higher CO 2 adsorption capacity. However, it is still unclear what role each variable in the textural properties plays in CO 2 adsorption. Herein, a deep neural network is trained as a generative model to direct the relationship between CO 2 adsorption of porous carbons and corresponding textural properties. The trained neural network is further employed as an implicit model to estimate its ability to predict the CO 2 adsorption capacity of unknown porous carbons. Interestingly, the practical CO 2 adsorption amounts are in good agreement with predicted values using surface area, micropore and mesopore volumes as the input values simultaneously. This unprecedented deep learning neural network (DNN) approach, a type of machine learning algorithm, exhibits great potential to predict gas adsorption and guide the development of next-generation carbons.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Quantifying discordance in the 2015 Planck CMB spectrum

We examine the internal consistency of the Planck 2015 cosmic microwave background (CMB) temperature anisotropy power spectrum. We show that tension exists between cosmological constant cold dark matter (ΛCDM) model parameters inferred from multipoles ℓ<1000 (roughly those accessible to Wilkinson Microwave Anisotropy Probe), and from ℓ⩾1000, particularly the CDM density, Ω{sub c}h{sup 2}, which is discrepant at 2.5σ for a Planck -motivated prior on the optical depth, τ=0.07±0.02. We find some parameter tensions to be larger than previously reported because of inaccuracy in the code used by the Planck Collaboration to generate model spectra. The Planck ℓ⩾1000 constraints are also in tension with low-redshift data sets, including Planck ’s own measurement of the CMB lensing power spectrum (2.4σ), and the most precise baryon acoustic oscillation scale determination (2.5σ). The Hubble constant predicted by Planck from ℓ⩾1000, H{sub 0}=64.1±1.7 km s{sup −1} Mpc{sup −1}, disagrees with the most precise local distance ladder measurement of 73.0±2.4 km s{sup −1} Mpc{sup −1} at the 3.0σ level, while the Planck value from ℓ<1000, 69.7±1.7 km s{sup −1} Mpc{sup −1}, is consistent within 1σ. A discrepancy between the Planck and South Pole Telescope high-multipole CMB spectra disfavors interpreting these tensions as evidence for new physics. We conclude that the parameters from the Planck high-multipole spectrum probably differ from the underlying values due to either an unlikely statistical fluctuation or unaccounted-for systematics persisting in the Planck data.

79 ASTRONOMY AND ASTROPHYSICS↗

Invertible neural networks for E3SM land model calibration and simulation

We apply an invertible neural network (INN) for E3SM land model calibration and simulation with eight parameters at the Missouri Ozark AmeriFlux forest site. INN provides bijective (two-way) mappings between inputs and outputs, thus it can solve probabilistic inverse problems and forward approximations simultaneously. We demonstrate INN's inverse and forward capability in both synthetic and real-data applications. Results indicate that INN produces accurate parameter posterior distributions similar to Markov Chain Monte Carlo sampling and it generates model outputs close to the forward model simulations. Additionally, both the inverse and forward evaluations in INN are computationally efficient which allows for rapid integration of observations for parameter estimation and fast model predictions.

Lu, Dan↗

Efficient sampling of constrained high-dimensional theoretical spaces with machine learning

Abstract Models of physics beyond the Standard Model often contain a large number of parameters. These form a high-dimensional space that is computationally intractable to fully explore. Experimental results project onto a subspace of parameters that are consistent with those observations, but mapping these constraints to the underlying parameters is also typically intractable. Instead, physicists often resort to scanning small subsets of the full parameter space and testing for experimental consistency. We propose an alternative approach that uses generative models to significantly improve the computational efficiency of sampling high-dimensional parameter spaces. To demonstrate this, we sample the constrained and phenomenological Minimal Supersymmetric Standard Models subject to the requirement that the sampled points are consistent with the measured Higgs boson mass. Our method achieves orders of magnitude improvements in sampling efficiency compared to a brute force search.

Hollingsworth, Jacob↗

Comprehensive, Quantitative Risk Assessment of CO 2 Geologic Sequestration (Final Technical Report)

A Quantitative Failure Modes and Effects Analysis (QFMEA) was developed to conduct comprehensive, quantitative risk assessments on CO 2 capture, transportation, and sequestration or use in deep saline aquifers, enhanced oil recovery operations, or enhanced coal bed methane operations. The model identifies and characterizes potential risks; identifies the likely failure modes, causes, effects and methods of detection; lists possible risk prevention and risk mitigation steps; estimates potential damage recovery costs, mitigation costs and costs savings resulting from mitigation; and ranks (prioritizes) risks according to the probability of failure, the severity of failure, the difficulty of early failure detection and the potential for fatalities. The QFMEA model generates the necessary information needed for effective project risk management. Diverse project information can be integrated into a concise, common format that allows comprehensive, quantitative analysis, by a cross-functional team of experts, to determine: What can possibly go wrong? How much will damage recovery cost? How can it be prevented or mitigated? What is the cost savings or benefit of prevention or mitigation? Which risks should be given highest priority for resolution? The QFMEA model can be tailored to specific projects and is applicable to new projects as well as mature projects. The model can be revised and updated as new information comes available. It accepts input from multiple sources, such as literature searches, site characterization, field data, computer simulations, analogues, process influence diagrams, probability density functions, financial analysis models, cost factors, and heuristic best practices manuals, and converts the information into a standardized format in an Excel spreadsheet. Process influence diagrams, geologic models, financial models, cost factors and an insurance schedule were developed to support the QFMEA model. Comprehensive, quantitative risk assessments were conducted on three (3) sites using the QFMEA model: (1) SACROC Northern Platform CO 2 -EOR Site in the Permian Basin, Scurry County, TX, (2) Pump Canyon CO 2 -ECBM Site in the San Juan Basin, San Juan County, NM, and (3) Farnsworth Unit CO 2 -EOR Site in the Anadarko Basin, Ochiltree County, TX. The sites were sufficiently different from each other to test the robustness of the QFMEA model.

54 ENVIRONMENTAL SCIENCES↗