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At least 109 records · Page 6

Bayesian optimization to design a novel x-ray shaping device

In radiation therapy, x-ray dose must be precisely sculpted to the tumor, while simultaneously avoiding surrounding organs at risk. This requires modulation of x-ray intensity in space and/or time. Typically, this is achieved using a multi leaf collimator (MLC) - a complex mechatronic device comprising over one hundred individually powered tungsten ‘leaves’ that move in or out of the radiation field as required. Here, an all-electronic x-ray collimation concept with no moving parts is presented, termed “SPHINX”: Scanning Pencil-beam High-speed Intensity-modulated X-ray source. SPHINX utilizes a spatially distributed bremsstrahlung target and collimator array in conjunction with magnetic scanning of a high energy electron beam to generate a plurality of small x-ray “beamlets.” A simulation framework was developed in Topas Monte Carlo incorporating a phase space electron source, transport through user defined magnetic fields, bremsstrahlung x-ray production, transport through a SPHINX collimator, and dose in water. This framework was completely parametric, meaning a simulation could be built and run for any supplied geometric parameters. This functionality was coupled with Bayesian optimization to find the best parameter set based on an objective function which included terms to maximize dose rate for a user defined beamlet width while constraining inter-channel cross talk and electron contamination. Designs for beamlet widths of 5, 7, and 10 mm 2 were generated. Each optimization was run for 300 iterations and took approximately 40 h on a 24-core computer. For the optimized 7-mm model, a simulation of all beamlets in water was carried out including a linear scanning magnet calibration simulation. Finally, a back-of-envelope dose rate formalism was developed and used to estimate dose rate under various conditions. The optimized 5–, 7–, and 10-mm models had beamlet widths of 5.1 , 7.2 , and 10.1 mm 2 and dose rates of 3574, 6351, and 10 015 Gy/C, respectively. The reduction in dose rate for smaller beamlet widths is a result of both increased collimation and source occlusion. For the simulation of all beamlets in water, the scanning magnet calibration reduced the offset between the collimator channels and beam centroids from 2.9 ±1.9 mm to 0.01 ±0.03 mm. A slight reduction in dose rate of approximately 2% per degree of scanning angle was observed. Based on a back-of-envelope dose rate formalism, SPHINX in conjunction with next-generation linear accelerators has the potential to achieve substantially higher dose rates than conventional MLC-based delivery, with delivery of an intensity modulated 100 x 100 mm 2 field achievable in 0.9 to 10.6 s depending on the beamlet widths used. Bayesian optimization was coupled with Monte Carlo modeling to generate SPHINX geometries for various beamlet widths. A complete Monte Carlo simulation for one of these designs was developed, including electron beam transport of all beamlets through scanning magnets, x-ray production and collimation, and dose in water. These results demonstrate that SPHINX is a promising candidate for sculpting radiation dose with no moving parts, and has the potential to vastly improve both the speed and robustness of radiotherapy delivery. A multi-beam SPHINX system may be a candidate for delivering magavoltage FLASH RT in humans.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

Updating Critical Temperature Calculations for Several Secondary High Explosives

In designing experiments where high explosives (HEs) are heated, it is important to have an understanding of where thermal runaway may occur. This determination is often done by using the Frank-Kamenetskii (FK) equation. For several highly-studied HEs, the parameters necessary for FK calculations are typically referenced from decades-old literature, and more recent experimental data have shown that these values require adjustment. For example, some of the legacy parameters dangerously overpredict the critical temperature by tens of degrees relative to more recent observed values. Herein, we first summarize historical results and highlight the importance of insulation effects when estimating critical temperatures for small samples, which is especially relevant to small-scale aging, compatibility, and characterization experiments. Here, we then present updated FK parameters for several common secondary CHNO HEs like RDX, HMX, PETN, and TATB, as well as the first reported values for 2,6-Diamino-3,5-dinitropyrazine-1-oxide (LLM-105). Our updated parameters produce critical-temperature curves that are consistent with both mm-scale differentical scanning calorimetry (DSC) measurements and cm-scale data from the Lawrence Livermore National Laboratory one-dimensional time-to-explosion (ODTX) experiment. In analyzing the critical temperature versus sample size curves derived from the transcendental FK equation, we discovered that these curves are remarkably well described by a simple power-law function with a universal power of 0.142 and a HE-specific scaling factor.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

Large-eddy simulation study on cycle-to-cycle variation of knocking combustion in a spark-ignition engine

The cycle-to-cycle variation in the knock intensity is commonly encountered under abnormal combustion conditions. The severity of these abnormal combustion events can vary significantly, and the efficiency of engines at high loads is limited in practice by heavy knocking phenomena. Since, a thorough analysis of such recurrent but non-cyclic phenomena via experiments alone becomes highly cumbersome, in the present work, a multi-cycle large-eddy simulation study was performed to quantitatively predict cyclic variability in the combustion process and cyclic knock intensity variability in a direct injection spark-ignition engine. To account for the turbulence-chemistry interaction effects on flame propagation, the G-equation combustion model was used. Detailed chemistry was solved outside the flame front with a toluene primary reference fuel skeletal kinetic mechanism. For both the mild knock and heavy knock conditions, the numerical results were validated against experimental measurements. Based on the simulation results, a correlation analysis was performed considering combustion phasing, peak cylinder pressure and maximum amplitude of pressure oscillation. Furthermore, a detailed three-dimensional spatial analysis illustrated the evolution of auto-ignition kernel development and propagation of pressure waves during knocking combustion for three typical cycles with different knock intensities. In this process, it was found that an early occurrence of auto-ignition in the end gas was prone to high knock intensity. Although multiple auto-ignition kernels were observed in different cycles, the degree of coupling between chemical heat release and pressure waves varied, thereby leading to different maximum amplitude of pressure oscillation values.

42 ENGINEERING↗

Neural network interatomic potential-driven analysis of phase stability in Ti–V alloys at the atomistic scale

The evolution of the ω phase in titanium–vanadium (Ti–V) alloys is critical for their mechanical properties, particularly in aerospace and biomedical applications. Here, this study employs a Rapid Artificial Neural Network (RANN) potential to model the ω phase evolution at the atomistic level, demonstrating a high degree of consistency with experimental observations, unlike the Modified Embedded Atom Method (MEAM), which fails to capture this phase transformation accurately. RANN simulations replicate key phenomena such as the nucleation of α precipitates at ω/β interfaces and accurate lattice orientations, enhancing our understanding of phase stability and transformation kinetics. The findings affirm that RANN potentials can significantly improve the prediction accuracy of complex material behaviors, offering a powerful tool for designing advanced materials with tailored properties such as solute effect in various stacking fault energies. This approach not only bridges the gap between theoretical predictions and empirical data but also sets a new direction for future research in materials science, emphasizing the integration of machine learning techniques in the development and optimization of new alloys.

36 MATERIALS SCIENCE↗

Anisotropic growth of Ni 2 (Cr,Mo) ordered phase in proton irradiated Ni-Cr-Mo alloys

Ni-Cr-Mo alloys are widely used in the nuclear industry as structural materials due to their high temperature strength and corrosion resistance. Ni-based alloys containing around 33 at.% (Cr+Mo) developed a long-range ordered Ni 2 (Cr,Mo) phase after thermal aging and/or irradiation. The ordering mechanism for thermally-aged Ni 2 (Cr,Mo) phase is well-understood, characterized to be sluggish, homogeneous, and isotropic. The ordering mechanism for irradiation-induced Ni 2 (Cr,Mo) phase is not fully understood, characterized as having rapid formation and demonstrating anisotropic precipitation. Here, this work elucidates the anisotropic precipitation and anisotropic precipitation mechanism of Ni 2 (Cr,Mo) after proton irradiation in Ni-Cr-Mo alloys. Selected area electron diffraction and bright-field scanning transmission electron microscopy imaging are used to image superlattice reflections from the ordered phase and irradiation-induced defects, respectively. A higher degree of anisotropic precipitation is observed with increasing dislocation loop and void size; a phenomenon not observed in thermally aged samples.

36 - MATERIALS SCIENCE↗

Comparison of Heterogeneous Hydroformylation of Ethylene and Propylene over RhCo 3 /MCM-41 Catalysts

The reaction mechanisms of heterogenous hydroformylation of ethylene and propylene were compared at 413-453 K using RhCo 3 /MCM-41 as catalysts. The reaction rates of propylene for both hydroformylation and the undesired side reaction of hydrogenation were found to be about one order of magnitude lower than those for ethylene in flow reactor studies. The difference in the kinetic behavior between ethylene and propylene was investigated by measuring the reaction orders and apparent activation energies, and these macro-kinetic observables were analyzed using the degree of rate control (DRC) method. In situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) experiments were performed to characterize the surface intermediates formed during the reactions. When the reactant was changed from ethylene to propylene, the IR peak corresponding to adsorbed CO exhibited a significant increase, while the IR peaks of alkyl group decreased in magnitude. Combined with the DRIFTS results, DRC analysis indicates that the first step of olefin hydroformylation, the formation of an alkyl group on the catalyst surface, plays a key role in the difference between ethylene and propylene. This step is kinetically non-relevant when ethylene is the reactant, but it is one of the rate-controlling steps for propylene. Furthermore, the low concentration of the adsorbed propyl group, which is a common intermediate shared by both hydroformylation and hydrogenation of propylene, decreases the rates of both reaction pathways as compared to ethylene.

10 SYNTHETIC FUELS↗

A comprehensive study on three typical photoacid generators using photoelectron spectroscopy and ab initio calculations

Conducting a comprehensive molecular-level evaluation of a photoacid generator (PAG) and its subsequent impact on lithography performance can facilitate the rational design of a promising 193 nm photoresist tailored to specific requirements. In this study, we integrated spectroscopy and computational techniques to meticulously investigate the pivotal factors of three prototypical PAG anions, p-toluenesulfonate (pTS - ), 2-(trifluoromethyl)benzene-1-sulfonate (TFMBS - ), and triflate (TF - ), in the lithography process. Our findings reveal a significant redshift in the absorption spectra caused by specific PAG anions, attributed to their involvement in electronic transition processes, thereby enhancing the transparency of the standard PAG cation, triphenylsulfonium (TPS + ), particularly at ~193 nm. Furthermore, the electronic stability of PAG anions can be enhanced by solvent effects with varying degrees of strength. Here we observed the lowest vertical detachment energy of 6.6 eV of pTS - in PGMEA solution based on the polarizable continuum model, which prevents anion loss at 193 nm lithography. In addition, our findings indicate gas-phase proton affinity values of 316.4 kcal/mol for pTS - , 308.1 kcal/mol for TFMBS - , and 303.2 kcal/mol for TF - , which suggest the increasing acidity strength, yet even the weakest acid pTS - is still stronger than strong acid HBr. The photolysis of TPS + -based PAG, TPS + ·pTS - , generated an excited state leading to homolysis bond cleavage with the lowest reaction energy of 83 kcal/mol. Overall, the PAG anion pTS - displayed moderate acidity, possessed the lowest photolysis reaction energy, and demonstrated an appropriate redshift. These properties collectively render it a promising candidate for an effective acid producer.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Spin and orbital excitations through the metal-to-insulator transition in Cd 2 Os 2 O 7 probed with high-resolution resonant inelastic x-ray scattering

Here, high-resolution resonant inelastic x-ray scattering measurements (ΔE=46meV) have been performed on Cd 2 Os 2 O 7 through the metal-to-insulator transition (MIT). A magnetic excitation at 125 meV evolves continuously through the MIT, in agreement with recent Raman-scattering results, and provides further confirmation for an all-in all-out magnetic ground state. Asymmetry of this feature is likely a result of coupling between the electronic and the magnetic degrees of freedom. We also observe a broad continuum of interband excitations centered at 0.3 eV energy loss. This is indicative of significant hybridization between Os 5d and O 2p states and the concurrent itinerant nature of the system. In turn, this suggests a possible breakdown of the free-ion model for Cd 2 Os 2 O 7 .

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Learning to Predict Arbitrary Quantum Processes

We present an efficient machine-learning (ML) algorithm for predicting any unknown quantum process ℰ over 𝑛 qubits. For a wide range of distributions 𝒟 on arbitrary 𝑛-qubit states, we show that this ML algorithm can learn to predict any local property of the output from the unknown process ℰ, with a small average error over input states drawn from 𝒟. The ML algorithm is computationally efficient even when the unknown process is a quantum circuit with exponentially many gates. Our algorithm combines efficient procedures for learning properties of an unknown state and for learning a low-degree approximation to an unknown observable. The analysis hinges on proving new norm inequalities, including a quantum analogue of the classical Bohnenblust-Hille inequality, which we derive by giving an improved algorithm for optimizing local Hamiltonians. Numerical experiments on predicting quantum dynamics with evolution time up to 10 6 and system size up to 50 qubits corroborate our proof. Overall, our results highlight the potential for ML models to predict the output of complex quantum dynamics much faster than the time needed to run the process itself.

quantum computation↗

Contrasting decays of 3-quasiparticle isomers in 173 W: A discerning test of 𝐾 conservation and 𝐾 mixing

Two isomers with K π = 19/2 + at 1876 keV with a half-life of 21(1) ns, and K π = 23/2− at 2159 keV with a half-life of 93(6) ns have been identified in 173 W, the lightest tungsten isotope to exhibit this phenomenon. An unusually high number of 13 branches have been established in the decay of the K π = 23/2 − isomer. This decay is associated with transitions involving significantly higher degrees of K forbiddenness and is in sharp contrast with the usual preferential deexcitation through branches, which are less forbidden, as is evident for the K π = 19/2 + state in 173 W. The inclusion of K-mixing effects, arising from band crossings in the sequences to which decays with larger degrees of K forbiddenness are observed, can qualitatively account for this contrasting behavior. These results provide a striking illustration of the competition between K conservation and K mixing in the decay of such isomers.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

BCSR on GPU: A Way Forward Extreme-scale Graph Processing on Accelerator-enabled Frontier Supercomputer

Handling large graphs in a distributed environment requires effective partitioning across processors and efficient management of local partitions. In 2D partitioning, local graphs often become too sparse, making memory-efficient data structures crucial. Using the Compressed Sparse Row (CSR) format wastes space, especially for > 83% of vertices with empty edges for the sparse graphs. This study explores bit-CSR (BCSR), a modified CSR representation, on GPUs to reduce memory usage in graph computations. We achieved 16.67% memory savings on a sparse rmat dataset with 268 million vertices and 357 million edges, without performance degradation, supported by both theoretical and experimental storage savings of 33%. However, we observed a 1.7× slowdown in degree lookup times due to bitwise operations on AMD CPUs. This analysis highlights the potential of BCSR on GPUs for improving Graph500 benchmark performance on GPU-accelerated systems, such as the Frontier supercomputer.

Sattar, Naw Safrin↗

Data Analytics Applied to Coal Fired Boilers for Detecting Leaks

Data analytics were used to detect boiler leaks from five different coal-fired boilers including both subcritical and supercritical systems. Discriminant functions were developed that detected leaks up to two weeks prior to forced plant shutdowns for repairs. The leaks were identified to occur at different sections of the boiler for each plant, including waterwalls, economizer and superheater using conventional process measurement data. Leaking conditions were detected with a high degree of confidence (≪ 1% misclassified observations) and were able to distinguish normal operations from those time periods with steam leaks even while operating the power plants in power cycling mode.Multivariable statistical analyses, including Principal Component (PCA), cluster, and Fischer Discriminant Analysis (FDA) were used to characterize the leak occurrence. Normal and operational states with steam leaks were provided in the original process datasets. These datasets were split into two different groups for training and validation purposes. The data were sorted chronologically, and every third observation was assigned to training the Discriminant Function Model (DFM) while the rest were reserved for validation. PCA was used to reduce dimensionality of the original datasets. Canonical and FDA analyses were used to investigate the relationship between process variables. The outcome of the analyses revealed that nearly 35,000 observations were classified correctly; less than 0.05% of total observations were misclassified to be leaking, i.e. both false positives and false negatives.

Indrawan, Natarianto↗

Atomic layer deposition of HfO 2 films using carbon-free tetrakis(tetrahydroborato)hafnium and water

Thin hafnium oxide films were prepared by atomic layer deposition using a carbon-free precursor, tetrakis(tetrahydroborato)hafnium [Hf(BH 4 )( 4 )], and H 2 O. Film growth was studied using an in situ quartz crystal microbalance and Fourier transform infrared spectroscopy measurements. Self-limiting growth was observed between 100 and 175 degrees C, but the thermal decomposition of the Hf precursor occurred at higher temperatures. The film properties were investigated using x-ray photoelectron spectroscopy, x-ray reflectivity, x-ray diffraction, ellipsometry, time-of-flight secondary ion mass spectrometry, and x-ray absorption spectroscopy. The as-deposited films were found to consist of an amorphous mixture of HfO 2 and B 2 O 3 , and had a lower density and lower refractive index compared to pure HfO 2 thin films. Finally, annealing the films to >750 degrees C yielded crystalline monoclinic HfO 2 with a density of 9g/cm 3 and a refractive index of 2.10.

36 MATERIALS SCIENCE↗

On completeness and generalized symmetries in quantum field theory

We review a notion of completeness in QFT arising from the analysis of basic properties of the set of operator algebras attached to regions. In words, this completeness asserts that the physical observable algebras produced by local degrees of freedom are the maximal ones compatible with causality. We elaborate on equivalent statements to this completeness principle such as the non-existence of generalized symmetries and the uniqueness of the net of algebras. We clarify that for non-complete theories, the existence of generalized symmetries is unavoidable and further that they always come in dual pairs with precisely the same “size”. Moreover, the dual symmetries are always broken together, be it explicitly or effectively. Finally, we comment on several issues raised in recent literature, such as the relationship between completeness and modular invariance, dense sets of charges, and absence of generalized symmetries in the bulk of holographic theories.

Astronomy & Astrophysics↗

Differential credibility assessment for statistical downscaling

Climate science is increasingly using (i) ensembles of climate projections from multiple models derived using different assumptions and/or scenarios and (ii) process-oriented diagnostics of model fidelity. Efforts to assign differential credibility to projections and/or models are also rapidly advancing. A framework to quantify and depict the credibility of statistically downscaled model output is presented and demonstrated. Here, the approach employs transfer functions in the form of robust and resilient generalized linear models applied to downscale daily minimum and maximum temperature anomalies at 10 locations using predictors drawn from ERA-Interim reanalysis and two global climate models (GCM; GFDL-ESM2M and MPI-ESM-LR). The downscaled time series are used to derive several impact relevant CLIMDEX temperature indices that are assigned credibility based on (1) the reproduction of relevant large-scale predictors by the GCMs (i.e. fraction of regression beta-weights derived from predictors that are well-reproduced) and (2) the degree of variance in the observations reproduced in the downscaled series following application of a new variance inflation technique. Credibility of the downscaled predictands varies across locations, between the two GCM and is generally higher for minimum temperature than maximum temperature. The differential credibility assessment framework demonstrated here is easy to use and flexible. It can be applied as is to inform decision makers regarding projection confidence, and/or extended to include other components of the transfer functions, and/or used to weight members of a statistically downscaled ensemble.

54 ENVIRONMENTAL SCIENCES↗

LANL PVC bag-out-bag replacement [Slides]

The US material at 8 months aging, at room temperature and exposed to gamma at 80 degrees C has been completed. The FTIR data does not show any discernible difference between the US APU-ether material and the British material. There are some differences and the curves of the FTIR, but the behavior shows that the US material is behaving comparable to the British material. The British Blue sample exposed to gamma at 70 degrees C did have an observable change in the contact angle. This was the only notable change between the two different types of polymer the TGA on both the US and the British had no observable difference. Mechanical testing samples (dog-bones) are being prepared for tensile, shear, and puncture testing. This data will be provided in later meetings. Mass spec testing is needed to measure the headspace gases that are being seen during the aging study to better health characterize the radiolytic degradation of the APU ether samples, this is also ongoing.

36 MATERIALS SCIENCE↗

DAISY Complement Protein ML-Ready Data

A total of 172 children from the DAISY study with multiple plasma samples collected over time, with up to 23 years of follow-up, were characterized via proteomics analysis. Of the children there were 40 controls and 132 cases. All 132 cases had measurements across time relative to IA. Sampling was not consistent for all children. There were 47 of the children who had samples taken and evaluated prior to IA (Pre-IA), and 131 children had measurements at or after IA, but prior to diagnosis of clinical T1D (Post-IA). The control children were frequency matched on HLA genotypes and age and sex with an observed lower frequency of first degree relatives within the control group versus the cases For machine learning the children that will develop islet autoantibodies the 40 control and 47 Pre-IA children were down-selected to a single sample time point. For the 40 control children this was the earliest sample collected and for the 47 Pre-IA children it was a random selection of the first or second time point prior to the detection of autoantibodies to assure the age distributions were not significantly different.

machine learning, proteomics, Type 1 Diabetes, com↗

DAISY Complement Protein ML-Ready Data

A total of 172 children from the DAISY study with multiple plasma samples collected over time, with up to 23 years of follow-up, were characterized via proteomics analysis. Of the children there were 40 controls and 132 cases. All 132 cases had measurements across time relative to IA. Sampling was not consistent for all children. There were 47 of the children who had samples taken and evaluated prior to IA (Pre-IA), and 131 children had measurements at or after IA, but prior to diagnosis of clinical T1D (Post-IA). The control children were frequency matched on HLA genotypes and age and sex with an observed lower frequency of first degree relatives within the control group versus the cases For machine learning the children that will develop islet autoantibodies the 40 control and 47 Pre-IA children were down-selected to a single sample time point. For the 40 control children this was the earliest sample collected and for the 47 Pre-IA children it was a random selection of the first or second time point prior to the detection of autoantibodies to assure the age distributions were not significantly different.

Webb-Robertson, Bobbie-Jo M↗