Silver clusters shape determination from in-situ XANES data
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99 GENERAL AND MISCELLANEOUS↗
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Atomic and Electronic Structure in MgO–SiO 2
Understanding disordered structure is difficult due to insufficient information in experimental data. Here, we overcome this issue by using a combination of diffraction and simulation to investigate oxygen packing and network topology in glassy (g-) and liquid (l-) MgO–SiO 2 based on a comparison with the crystalline topology. We find that packing of oxygen atoms in Mg 2 SiO 4 is larger than that in MgSiO 3 , and that of the glasses is larger than that of the liquids. Moreover, topological analysis suggests that topological similarity between crystalline (c)- and g-(l-) Mg 2 SiO 4 is the signature of low glass-forming ability (GFA), and high GFA g-(l-) MgSiO 3 shows a unique glass topology, which is different from c-MgSiO 3 . We also find that the lowest unoccupied molecular orbital (LUMO) is a free electron-like state at a void site of magnesium atom arising from decreased oxygen coordination, which is far away from crystalline oxides in which LUMO is occupied by oxygen’s 3s orbital state in g- and l-MgO–SiO 2 , suggesting that electronic structure does not play an important role to determine GFA. We finally concluded the GFA of MgO–SiO 2 binary is dominated by the atomic structure in terms of network topology.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Subnanometer Substructures in Nanoassemblies Formed from Clusters under a Reactive Atmosphere Revealed Using Machine Learning
Size-selected clusters, soft-landed on an oxide substrate, is a promising and highly tunable material for heterogeneous catalysis. Agglomeration of the deposited clusters, however, leads to changes in the particle properties and structure. The latter for such cluster assemblies can also be different from that in self standing nanoparticles of similar sizes. To monitor the formation of such complex materials, in situ studies at different length scales are required. Toward that goal, we combined small-angle X-ray scattering (SAXS), X-ray absorption near-edge structure (XANES) spectroscopy, ab initio simulations, and machine learning (artificial neural network) techniques. We detected significant differences between the sizes of particle agglomerates, as probed by SAXS, and the sizes of locally ordered regions, as seen by XANES. We interpret these differences as an evidence for the fractal, grape-cluster-like structure of the agglomerates; thus, XANES and SAXS provide highly complementary structural information. This finding can have a profound effect on our understanding of particle sintering and assembly processes and of structure properties relationship in ultradispersed metal catalysts in reaction conditions.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Tetris-inspired detector with neural network for radiation mapping
Abstract Radiation mapping has attracted widespread research attention and increased public concerns on environmental monitoring. Regarding materials and their configurations, radiation detectors have been developed to identify the position and strength of the radioactive sources. However, due to the complex mechanisms of radiation-matter interaction and data limitation, high-performance and low-cost radiation mapping is still challenging. Here, we present a radiation mapping framework using Tetris-inspired detector pixels. Applying inter-pixel padding for enhancing contrast between pixels and neural networks trained with Monte Carlo (MC) simulation data, a detector with as few as four pixels can achieve high-resolution directional prediction. A moving detector with Maximum a Posteriori (MAP) further achieved radiation position localization. Field testing with a simple detector has verified the capability of the MAP method for source localization. Our framework offers an avenue for high-quality radiation mapping with simple detector configurations and is anticipated to be deployed for real-world radiation detection.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Linewidth broadening and tunneling of excitons bound to N pairs in dilute GaAs:N
The exciton bound to a pair of nitrogen atoms situated at nearby lattice sites in dilute GaAs:N provides an energetically uniform electronic system, spectrally distinct from pairs with larger or smaller separations, and can even be grown with a uniform pair orientation in the crystal. We use photoluminescence excitation spectroscopy on an ensemble of N pairs to study the narrow continuous energy distribution within two of the individual exchange- and symmetry-split exciton states. Inhomogeneous linewidths of 50-60 ueV vary across the crystal on a mesoscopic scale and can be 30 ueV at microscopic locations indicating that the homogeneous linewidth inferred from previous time-domain measurements is still considerably broadened. While excitation and emission linewidths are similar, results show a small energy shift between them indicative of exciton transfer via phonon-assisted tunneling between spatially separated N pairs. We numerically simulate the tunneling in a spatial network of randomly distributed pairs having a normal distribution of bound exciton energies. Comparing the ensemble excitation-emission energy shift with the measured results shows that the transfer probability is higher than expected from the dilute pair concentration and what is known of the exciton wavefunction spatial extent. Both the broadening and the exciton transfer have implications for the goal of pair-bound excitons as a single- or multi-qubit system.
36 MATERIALS SCIENCE↗
Axion string dynamics I: 2+1D
If the axion exists and if the initial axion field value is uncorrelated at causally disconnected points, then it should be possible to predict the efficiency of cosmological axion production, relating the axionic dark matter density to the axion mass. The main obstacle to making this prediction is correctly treating the axion string cores. We develop a new algorithm for treating the axionic string cores correctly in 2+1 dimensions. When the axionic string cores are given their full physical string tension, axion production is about twice as efficient as in previous simulations. We argue that the string network in 2+1 dimensions should behave very differently than in 3+1 dimensions, so this result cannot be simply carried over to the physical case. We outline how to extend our method to 3+1D axion string dynamics.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
PySFD: comprehensive molecular insights from significant feature differences detected among many simulated ensembles
Motivation Many modeling analyses of molecular dynamics (MD) simulations are based on a definition of states that can be (groups of) clusters of simulation frames in a feature space composed of molecular coordinates. With increasing dimension of this feature space (due to the increasing size or complexity of a simulated molecule), it becomes very difficult to cluster the underlying MD data and estimate a statistically robust model. To mitigate this “curse of dimensionality”, one can reduce the feature space, e.g., with principal component or time-lagged independent component analysis transformations, focusing the analysis on the most important modes of transitions. In practice, however, all these reduction strategies may neglect important molecular details that are susceptible to experimental verification. Results To recover such molecular details, I have developed PySFD (Significant Feature Differences analyzer for Python), a multi-processing software package that efficiently selects significantly different features of any user-defined feature type among potentially many different simulated state ensembles, such as meta-stable states of a Markov State Model (MSM). Applying PySFD on MSMs of an aggregate of 300 microseconds MD simulations recently performed on the major histocompatibility complex class II (MHCII) protein, I demonstrate how this toolkit can extract and visualize valuable mechanistic information from big MD simulation data, e.g., in form of networks of dynamic interaction changes connecting functionally relevant sites of a protein complex.
Biochemistry & Molecular Biology↗
Expanding Medicaid to Reduce Human Immunodeficiency Virus Transmission in Houston, Texas
Context: Medicaid expansion has been nationally shown to improve engagement in the human immunodeficiency virus (HIV) treatment and prevention continua, which are vital steps to stopping the HIV epidemic. New HIV infections in the United States are disproportionately concentrated among young Black men who have sex with men (YBMSM). Houston, TX, is the most populous city in the Southern United States with a racially/ethnically diverse population that is located in 1 of 11 US states that have not yet expanded Medicaid coverage as of 2021. Methods: An agent-based model that incorporated the sexual networks of YBMSM was used to simulate improved antiretroviral treatment and pre-exposure prophylaxis (PrEP) engagement through Medicaid expansion in Houston, TX. Analyses considered the HIV incidence (number of new infections and as a rate metric) among YBMSM over the next 10 years under Medicaid expansion as the primary outcome. Additional scenarios, involving viral suppression and PrEP uptake above the projected levels achieved under Medicaid expansion, were also simulated. Results: The baseline model projected an HIV incidence rate of 4.96 per 100 person years (py) and about 368 new annual HIV infections in the 10th year. Improved HIV treatment and prevention continua engagement under Medicaid expansion resulted in a 14.9% decline in the number of annual new HIV infections in the 10th year. Increasing viral suppression by an additional 15% and PrEP uptake by 30% resulted in a 44.0% decline in new HIV infections in the 10th year, and a 27.1% decline in cumulative infections across the 10 years of the simulated intervention. Findings: Simulation results indicate that Medicaid expansion has the potential to reduce HIV incidence among YBMSM in Houston. Achieving HIV elimination objectives, however, might require additional effective measures to increase antiretroviral treatment and PrEP uptake beyond the projected improvements under expanded Medicaid.
60 APPLIED LIFE SCIENCES↗
Inference of three-dimensional hot-spot and shell morphology in inertial confinement fusion experiments using a convolutional neural network
The performance of inertial confinement fusion (ICF) implosions is sensitive to the three-dimensional (3D) morphology of the hot-spot and shell configurations. The ability to infer shell-mass uniformity and reconstruct 3D hot spots is crucial for quantifying the degradation of ignition criteria and improving symmetry in ICF implosion experiments. In this work, we present a deep-learning convolutional neural network (CNN) for reconstructing 3D hot-spot and shell structures for ICF capsules. The 3D geometry of the hot spot is reconstructed from x-ray images measured from multiple lines of sight on OMEGA. The shell configuration is inferred indirectly through machine learning using a convolutional neural network extensively trained on a dec3d simulation database. This simulation-dependent approach yields consistent agreement between reconstructed 3D shell densities and machine-learning optimized dec3d simulation results. This work demonstrates a CNN framework that successfully reconstructs 3D capsule structures from two-dimensional images in ICF implosions.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
KamNet: An integrated spatiotemporal deep neural network for rare event searches in KamLAND-Zen
Rare event searches allow us to search for new physics at energy scales inaccessible with other means by leveraging specialized large-mass detectors. Machine learning provides a new tool to maximize the information provided by these detectors. The information is sparse, which forces these algorithms to start from the lowest level data and exploit all symmetries in the detector to produce results. In this work we present KamNet, which harnesses breakthroughs in geometric deep learning and spatiotemporal data analysis to maximize the physics reach of KamLAND-Zen, a kiloton scale spherical liquid scintillator detector searching for 0νββ. Using a simplified background model for KamLAND, we show that KamNet outperforms a conventional convolutional neural network (CNN) on benchmarking Monte Carlo simulations with an increasing level of robustness. Using simulated data, we then demonstrate KamNet's ability to increase KamLAND-Zen's sensitivity to 0νββ and 2νββ decay to excited states. A key component of this work is the addition of an attention mechanism to elucidate the underlying physics KamNet is using for the background rejection.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Global topology of contact force networks: Insight into shear thickening suspensions
Not Available
Edens, Lance E.↗
Grid-Forming Frequency Shaping Control for Low-Inertia Power Systems
As power systems transit to a state of high renewable penetration, little or no presence of synchronous generators makes the prerequisite of well-regulated frequency for grid-following inverters unrealistic. As such, there is a trend to resort to grid-forming inverters which set frequency directly. We propose a novel grid-forming frequency shaping control that is able to shape the aggregate system frequency dynamics into a first-order one with the desired steady-state frequency deviation and Rate of Change of Frequency (RoCoF) after a sudden power imbalance. The no overshoot property resulting from the first-order dynamics allows the system frequency to monotonically move towards its new steady-state without experiencing frequency Nadir, which largely improves frequency security. We prove that our grid-forming frequency-shaping control renders the system internally stable under mild assumptions. The performance of the proposed control is verified via numerical simulations on a modified Icelandic Power Network test case.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Destabilizing a Social Network Model via Intrinsic Feedback Vulnerabilities
Social influence plays a significant role in shaping individual sentiments and actions, particularly in a world of ubiquitous digital interconnection. The rapid development of generative artificial intelligence (AI) has given rise to well-founded concerns regarding the potential implementation of radicalization techniques in social media. Motivated by these developments, we present a case study investigating the effects of small but intentional perturbations on a simple social network. We employ Taylor's classic model of social influence and tools from robust control theory (most notably the Dynamical Structure Function (DSF)), to identify perturbations that qualitatively alter the system's behavior while remaining as unobtrusive as possible. We examine two such scenarios: perturbations to an existing link and perturbations that introduce a new link to the network. In each case, we identify destabilizing perturbations of minimal norm and simulate their effects. Remarkably, we find that small but targeted alterations to network structure may lead to the radicalization of all agents, exhibiting the potential for large-scale shifts in collective behavior to be triggered by comparatively minuscule adjustments in social influence. Given that this method of identifying perturbations that are innocuous yet destabilizing applies to any suitable dynamical system, our findings emphasize a need for similar analyses to be carried out on real systems (e.g., real social networks), to identify the places where such dynamics may already exist.
Rogers, Lane [ORNL]↗
Reinforcement Learning of Structured Stabilizing Control for Linear Systems With Unknown State Matrix
This paper delves into designing feedback control gains for a continuous-time linear quadratic regulator (LQR) problem that is constrained to certain predefined structure with unknown state matrix. We bring forth the ideas from reinforcement learning (RL) in conjunction with sufficient stability and performance guarantees in order to design these structured gains using the trajectory measurements of states and controls. Here we first formulate a model-based framework using dynamic programming (DP) to embed the structural constraint to the LQR gain computation in the continuous-time setting, and then subsequently, formulate a policy iteration RL algorithm that can alleviate the requirement of known state matrix in conjunction with maintaining the feedback gain structure. The design enables a distributed learning control design which is necessary for many large-scale cyber-physical systems. Theoretical guarantees are provided for stability and convergence of the structured reinforcement learning (SRL) algorithm. We validate our theoretical results with numerical simulations on a multi-agent networked linear time-invariant (LTI) dynamic system.
42 ENGINEERING↗
Improved Gas Plume Identification Using Nearest Neighbor Methods for Background Estimation
Longwave infrared (LWIR) hyperspectral imaging (HSI) can be used for many tasks in remote sensing, including detecting and identifying effluent gases by LWIR sensors on airborne platforms. Identification is used after detection to increase confidence in weakly detected plumes, reduce false positives from detection, and distinguish between similar and confounding material signatures. Background estimation is an important step used to reveal the unique spectral characteristics of the detected gas, allowing the identification model to determine what the gas is specifically. The importance of proper background estimation increases when dealing with weak signals, large libraries of gases of interest, and uncommon or heterogeneous backgrounds. In this article, we propose two methods for background estimation: a novel k-nearest segments (KNS) algorithm and the standard k-nearest neighbors (KNN) algorithm. We test our methods and three existing background estimation methods for comparison against global background estimation to determine which performs best at estimating the true background radiance under a plume and for increasing identification confidence using a neural network classification model. We compare the different methods using 640 simulated weak plumes in an urban environment. For identification, our KNS algorithm improves median neural network identification confidence by 53.2%. For background radiance estimation, the KNN algorithm provides a median of 49 times less RMSE than global background estimation. Furthermore, KNN is the easiest method to tune for different plumes, making it an excellent “out of the box” background estimator.
47 OTHER INSTRUMENTATION↗
Scaling the Ion Trap Quantum Processor
Trapped atomic ions are standards for quantum information processing, serving as quantum memories, hosts of quantum gates in quantum computers and simulators, and nodes of quantum communication networks. Quantum bits based on trapped ions enjoy a rare combination of attributes: They have exquisite coherence properties, they can be prepared and measured with nearly 100% efficiency, and they are readily entangled with each other through the Coulomb interaction or remote photonic interconnects. The outstanding challenge is the scaling of trapped ions to hundreds or thousands of qubits and beyond, at which scale quantum processors can outperform their classical counterparts in certain applications. We review the latest progress and prospects in that effort, with the promise of advanced architectures and new technologies, such as microfabricated ion traps and integrated photonics.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
A new way to unravel the $^{12}$C($\alpha $,$\gamma $)$^{16}$O cross section components using neural networks
The $^{12}$C($\alpha $,$\gamma $)$^{16}$O reaction rate is crucial in determining the carbon-to-oxygen abundance ratio in stellar nucleosynthesis. Measuring this reaction’s cross section at stellar energies is challenging due to its extremely small value, approximately 10 -17 barn at E c.m. = 300 keV. To address this, R-matrix calculations are employed to extrapolate data to lower energies, requiring a comprehensive understanding of each contribution to the cross section. The dominant contributions to the cross section at stellar energies arise from electric dipole (E1) and electric quadrupole (E2) transitions to the ground state of 16 O, along with a significant cascade contribution. Traditionally, these contributions have been separated using the γ-ray angular distribution. In this work, we propose a novel technique using the energy distribution of the 16 O recoils at the focal plane. This method involves a neural network trained on detailed Monte Carlo simulations of the energy distribution of recoils transported through the recoil mass separator ERNA. This approach enables the simultaneous determination of all three contributions with errors around 10% in the energy range E c.m. = 1.0–2.2 MeV. In conclusion, by employing this new technique, we aim to significantly improve the accuracy of determining the cross section of the $^{12}$C($\alpha $,$\gamma $)$^{16}$O reaction at astrophysical energies.
Astrochemistry↗
Multi Infrastructure Control and Optimization Toolkit, Resilient Design Module (MICOT-RDT), version 2.X
MICOT is a tool for optimizing and controlling infrastructure systems. In includes modules for optimizing the operations of an infrastructure structure (for example optimal dispatch), designing infrastructure systems, restoring infrastructures systems, resiliency, preparing for natural disasters, interdicting networks, state estimation, sensor placement, and simulation of infrastructure systems. It implements algorithms developed at LANL that have been published in the academic community. This is a release of the of resilient design module of the MICOT.
Bent, Russell↗