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At least 649 records · Page 36

Inelastic scattering in isotopologues of O 2 –Ar: the effects of mass, symmetry, and density of states

The two species considered here, O 2 (oxygen molecule) and Ar (argon-atom), are both abundant components of Earth's atmosphere and hence familiar collision partners in this medium. O 2 is quite reactive and extensively involved in atmospheric chemistry, including Chapman's cycle of the formation and destruction of ozone; while Ar, like N 2 , typically plays the nevertheless crucial role of inert collider. Inert species can provide stabilization to metastable encounter-complexes through the energy transfer associated with inelastic collisions. The interplay of collision frequency and energy transfer efficiency, with state lifetimes and species concentrations, contributes to the rich and varied chemistry and dynamics found in diverse environments ranging from planetary atmospheres to the interstellar and circumstellar media. The nature and density of bound and resonance states, coupled electronic states, symmetry, and nuclear spin-statistics can all play a role. Here, we systematically investigate some of those factors by looking at the O 2 –Ar system, comparing rigorous quantum-scattering calculations for the 16 O 16 O– 40 Ar, 18 O 16 O– 40 Ar, and 18 O 18 O– 40 Ar isotope combinations. A new accurate potential energy surface was constructed for this purpose holding the O 2 bond distance at its vibrationally averaged distance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Galaxy assembly bias and large-scale distribution: a comparison between IllustrisTNG and a semi-analytic model

In this work, we compare large scale structure observables for stellar mass selected samples at z = 0, as predicted by two galaxy models, the hydrodynamical simulation IllustrisTNG and the Santa-Cruz semi-analytic model (SC-SAM). Although both models have been independently calibrated to match observations, rather than each other, we find good agreement between the two models for two-point clustering and galaxy assembly bias signatures. The models also show a qualitatively similar response of occupancy and clustering to secondary halo parameters other than mass, such as formation history and concentration, although with some quantitative differences. Thus, our results demonstrate that the galaxy–halo relationships in SC-SAM and TNG are quite similar to first order. However, we also find areas in which the models differ. For example, we note a strong correlation between halo gas content and environment in TNG, which is lacking in the SC-SAM, as well as differences in the occupancy predictions for low-mass haloes. Moreover, we show that higher order statistics, such as cumulants of the density field, help us to accurately describe the galaxy distribution and discriminate between models that show degenerate behaviour for two-point statistics. Our results suggest that SAMs are a promising cost-effective and intuitive method for generating mock catalogues for next generation cosmological surveys.

79 ASTRONOMY AND ASTROPHYSICS↗

Influence of Added Salt on Chain Conformations in Poly(ethylene oxide) Melts: SANS Analysis with Complications

Poly(ethylene oxide) (PEO)-based electrolytes have gained increasing attention in both the rechargeable battery industry and fundamental research, but any influence of the ions on the polymer conformation is not fully understood. Small-angle neutron scattering (SANS) is a powerful means to determine single-polymer chain conformations. We conducted SANS experiments on 50:50 blends of perdeuterated PEO (dPEO) and hydrogenous PEO (hPEO) of two different molecular weights (20 and 40 kDa) doped with two different salts (LiTFSI and LiClO 4 ) at various concentrations. An additional measurement with LiI is also reported. The scattering profiles of salt-doped dPEO/hPEO blends were measured, and the incoherent scattering from dPEO and hPEO doped with salt was measured separately. A strong low q upturn was observed in some of the blends and in the pure dPEO homopolymers. The statistical segment lengths of PEO–salt mixtures were derived using a Kratky analysis. The segment length decreases modestly with increasing amounts of LiTFSI but apparently first increases then decreases with added LiClO 4 . At the highest salt concentration examined (r = [Li+]/[EO] = 0.125), a roughly 10% decrease in the statistical segment length was observed in both LiTFSI and LiClO 4 -doped PEOs, for both molecular weights. This work confirms that added salt causes a contraction of the PEO chain dimensions, but the effect appears slightly weaker than reported recently. Certain difficulties in data analysis are also discussed, including the unexplained low q scattering even in pure deuterated polymers, uncertainties in background subtraction, and inappropriate application of the Debye function.

36 MATERIALS SCIENCE↗

The Sagittarius stream in Gaia Early Data Release 3 and the origin of the bifurcations

The Sagittarius dwarf spheroidal (Sgr) is a dissolving galaxy being tidally disrupted by the Milky Way (MW). Its stellar stream still poses serious modelling challenges, which hinders our ability to use it effectively as a prospective probe of the MW gravitational potential at large radii. Our goal is to construct the largest and most stringent sample of stars in the stream with which we can advance our understanding of the Sgr-MW interaction, focusing on the characterization of the bifurcations. We improved on previous methods based on the use of the wavelet transform to systematically search for the kinematic signature of the Sgr stream throughout the whole sky in the Gaia data. We then refined our selection via the use of a clustering algorithm on the statistical properties of the colour-magnitude diagrams. Our final sample contains more than 700 000 candidate stars and is three times larger than previous Gaia samples. With it, we have been able to detect the bifurcation of the stream in both the northern and southern hemispheres, which requires four branches (two bright and two faint) to fully describe the system. We present the detailed proper motion distribution of the trailing arm as a function of the angular coordinate along the stream, showing, for the first time, the presence of a sharp edge (on the side of the small proper motions) beyond which there are no Sgr stars. We also characterize the correlation between kinematics and distance. Finally, the chemical analysis of our sample shows that the faint branch of the bifurcation is more metal poor than the bright. We provide analytical descriptions for the proper motion trends as well as for the sky distribution of the four branches of the stream. Based on our analysis, we interpret the bifurcations as a misaligned overlap of the material stripped at the antepenultimate pericentre (faint branches) with the stars ejected at the penultimate pericentre (bright branch), given that Sgr just went through its perigalacticon. The source of this misalignment is still unknown, but we argue that models with some internal rotation in the progenitor – at least during the time of stripping of the stars that are now in the faint branches – are worth exploring.

79 ASTRONOMY AND ASTROPHYSICS↗

Eigenstate thermalization and disappearance of quantum many-body scar states in weakly interacting fermion systems

The recent discovery of quantum many-body scar states has revealed the possibility of having states with low entanglement that violate the eigenstate thermalization hypothesis in nonintegrable systems. Eigenstates with low entanglement entropy are rare but naturally exist in the integrable system of free fermions. Here, we demonstrate analytically that these atypical states would be always eliminated when an arbitrary weak interaction is introduced between the fermions. In particular, we show that the probability of having a many-body scar state with entanglement entropy satisfying a sub-volume scaling law decreases double exponentially as the system size. Furthermore, our results provide a quantitative argument for the disappearance of scar states in interacting fermion systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Meeting Global Health Needs via Infectious Disease Forecasting: Development of a Reliable Data-Driven Framework

Infectious diseases (IDs) have a significant detrimental impact on global health. Timely and accurate ID forecasting can result in more informed implementation of control measures and prevention policies. To meet the operational decision-making needs of real-world circumstances, we aimed to build a standardized, reliable, and trustworthy ID forecasting pipeline and visualization dashboard that is generalizable across a wide range of modeling techniques, IDs, and global locations. We forecasted 6 diverse, zoonotic diseases (brucellosis, campylobacteriosis, Middle East respiratory syndrome, Q fever, tick-borne encephalitis, and tularemia) across 4 continents and 8 countries. We included a wide range of statistical, machine learning, and deep learning models (n=9) and trained them on a multitude of features (average n=2326) within the One Health landscape, including demography, landscape, climate, and socioeconomic factors. The pipeline and dashboard were created in consideration of crucial operational metrics—prediction accuracy, computational efficiency, spatiotemporal generalizability, uncertainty quantification, and interpretability—which are essential to strategic data-driven decisions. While no single best model was suitable for all disease, region, and country combinations, our ensemble technique selects the best-performing model for each given scenario to achieve the closest prediction. For new or emerging diseases in a region, the ensemble model can predict how the disease may behave in the new region using a pretrained model from a similar region with a history of that disease. The data visualization dashboard provides a clean interface of important analytical metrics, such as ID temporal patterns, forecasts, prediction uncertainties, and model feature importance across all geographic locations and disease combinations. As the need for real-time, operational ID forecasting capabilities increases, this standardized and automated platform for data collection, analysis, and reporting is a major step forward in enabling evidence-based public health decisions and policies for the prevention and mitigation of future ID outbreaks.

60 APPLIED LIFE SCIENCES↗

Modeling fluid phase equilibria of carbon dioxide-methanol binary system

Accurate fluid phase equilibria modeling of carbon dioxide-methanol binary system is essential for numerous industrial applications. Prior modeling studies were limited in scope in terms of temperature and pressure ranges. In this study, the phase equilibria behavior of the binary system is modeled with cubic equations-of-state (EoS) including Peng-Robinson (PR) and Soave-Redlich-Kwong with various mixing rules, Predictive Soave-Redlich-Kwong, Cubic Plus Association, and Perturbed-Chain Statistical Associating Fluid Theory. Among them, the classical PR EoS and its variants yield the overall best results in representing the phase behavior at temperature above 330 K. Furthermore, PR incorrectly predicts two liquid phases when the CO 2 mole fraction exceeds ~ 0.4 at temperature lower than 330 K. Raoult’s law with the classical nonrandom two-liquid excess Gibbs energy model is recommended for the low temperature conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Closing the Gap Between Modeling and Experiments in the Self-assembly of Biomolecules at Interfaces and in Solution

Molecular self-assembly is a powerful tool in materials design, wherein non-covalent interactions like electrostatic, hydrophobic, hydrogen bonding, and van der Waals can be exploited to produce supramolecular nanostructures that are functional and highly tunable. Biomolecules are attractive building blocks, as they are biocompatible, biodegradable and adopt a wide array of higher order structures. Moreover, naturally occurring protein systems display a manifold of structures and interactions that can be replicated in synthetic biomolecules. In this perspective, we highlight advances in multiscale simulation techniques across broad spatiotemporal scales that can aid in characterizing self-assembly of hybrid and hierarchical bionanomaterial systems, with an emphasis on physics-based simulation approaches currently employed to study biomolecules at mineral interfaces. The power of these approaches is highlighted across a few recent areas where molecular simulations have advanced our understanding of self-assembly spanning peptides to protein self-assembly. Looking forward, we discuss how in the near future emerging methods in statistical and machine learning will advance this research field in all areas from expanding the capabilities of physics-based simulation methods to enabling new analyses of high throughput experiments. These advances will pave the way for understanding the molecular recognition patterns in systems that are dictated by self-assembly - biomineralizing peptides, hierarchical peptoids, and large protein assemblies, and will aid in the development of a new synthesis science for achieving precise molecular control in materials design

Sampath, Janani↗

Gas-Phase Formation of C 5 H 6 Isomers via the Crossed Molecular Beam Reaction of the Methylidyne Radical (CH; X 2 Π) with 1,2-Butadiene (CH 3 CHCCH 2 ; X 1 A')

The bimolecular gas-phase reaction of the methylidyne radical (CH; X 2 Π) with 1,2-butadiene (CH 2 CCHCH 3 ; X 1 A’) was investigated at a collision energy of 20.6 kJ mol -1 under single collision conditions. Combining our laboratory data with high-level electronic structure calculations, we reveal that this bimolecular reaction proceeds through the barrierless addition of the methylidyne radical to the carbon-carbon double bonds of 1,2-butadiene leading to doublet C 5 H 7 intermediates. These collision adducts undergo a non-statistical unimolecular decomposition through atomic hydrogen elimination to at least the cyclic 1-vinyl-cyclopropene (p5/p26), 1-methyl-3-methylenecyclopropene (p28), and 1,2-bis(me-thylene)cyclopropane (p29) in overall exoergic reactions. Lastly, the barrierless nature of this bimolecular reaction suggests that these cyclic C 5 H 6 isomers might be viable targets to be searched in cold molecular clouds like TMC-1.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Atomistically-informed modeling of point defect clustering and evolution in irradiated ThO 2

A cluster dynamics (CD) model has been developed to investigate the nucleation and growth of point defect clusters, i.e., interstitial prismatic loops and nanoscale and sub-nanoscale voids, in ThO 2 during irradiation by energetic particles. The model considers cluster off-stoichiometry due to the asymmetry of point defect generation on the O and Th sublattices under irradiation, as well as the point defect diffusivities and the defect binding energies to clusters. The energies were established using detailed molecular dynamics simulations considering the statistical variability of cluster configuration. A high-order adaptive time-integration has been used to solve the model. The predicted loop density and their average size is in good agreement with reported experimental observations for proton irradiated ThO 2 at 600°C. In conclusion, the model did not predict void evolution due to the sluggish kinetics of cation vacancies, explaining the absence of voids in proton irradiated ThO 2 (and other oxides) at relatively low temperatures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Sequence Design of Random Heteropolymers as Protein Mimics

Random heteropolymers (RHPs) have been computationally designed and experimentally shown to recapitulate protein-like phase behavior and function. However, unlike proteins, RHP sequences are only statistically defined and cannot be sequenced. Recent developments in reversible-deactivation radical polymerization allowed simulated polymer sequences based on the well-established Mayo–Lewis equation to more accurately reflect ground-truth sequences that are experimentally synthesized. This led to opportunities to perform bioinformatics-inspired analysis on simulated sequences to guide the design, synthesis, and interpretation of RHPs. We compared batches on the order of 10000 simulated RHP sequences that vary by synthetically controllable and measurable RHP characteristics such as chemical heterogeneity and average degree of polymerization. Our analysis spans across 3 levels: segments along a single chain, sequences within a batch, and batch-averaged statistics. We discuss simulator fidelity and highlight the importance of robust segment definition. Examples are presented that demonstrate the use of simulated sequence analysis for in-silico iterative design to mimic protein hydrophobic/hydrophilic segment distributions in RHPs and compare RHP and protein sequence segments to explain experimental results of RHPs that mimic protein function. To facilitate the community use of this workflow, the simulator and analysis modules have been made available through an open source toolkit, the RHPapp.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Resonant Multiple-Phonon Absorption Causes Efficient Anti-Stokes Photoluminescence in CsPbBr 3 Nanocrystals

Lead halide perovskite nanocrystals, such as CsPbBr 3 , exhibit efficient photoluminescence (PL) up-conversion, also referred to as anti-Stokes photoluminescence (ASPL). This is a phenomenon where irradiating nanocrystals up to 100 meV below gap results in higher energy band edge emission. Most surprising is that ASPL efficiencies approach unity and involve single-photon interactions with multiple phonons. This is unexpected given the statistically disfavored nature of multiple-phonon absorption. Here, in this study, we report and rationalize near-unity anti-Stokes photoluminescence efficiencies in CsPbBr 3 nanocrystals and attribute them to resonant multiple-phonon absorption by polarons. The theory explains paradoxically large efficiencies for intrinsically disfavored, multiple-phonon-assisted ASPL in nanocrystals. Moreover, the developed microscopic mechanism has immediate and important implications for applications of ASPL toward condensed phase optical refrigeration.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Constraining the Milky Way Mass Profile with Phase-space Distribution of Satellite Galaxies

We estimate the Milky Way (MW) halo properties using satellite kinematic data including the latest measurements from Gaia DR2. With a simulation-based 6D phase-space distribution function (DF) of satellite kinematics, we can infer halo properties efficiently and without bias, and handle the selection function and measurement errors rigorously in the Bayesian framework. Applying our DF from the EAGLE simulation to 28 satellites, we obtain an MW halo mass of $M={1.23}_{-0.18}^{+0.21}\times {10}^{12}{M}_{\odot }$ and a concentration of $c={9.4}_{-2.1}^{+2.8}$ with the prior based on the M–c relation. The inferred mass profile is consistent with previous measurements but with better precision and reliability due to the improved methodology and data. Potential improvement is illustrated by combining satellite data and stellar rotation curves. Using our EAGLE DF and best-fit MW potential, we provide much more precise estimates of the kinematics for those satellites with uncertain measurements. Compared to the EAGLE DF, which matches the observed satellite kinematics very well, the DF from the semi-analytical model based on the dark-matter-only simulation Millennium II (SAM-MII) over-represents satellites with small radii and velocities. We attribute this difference to less disruption of satellites with small pericenter distances in the SAM-MII simulation. Finally, by varying the disruption rate of such satellites in this simulation, we estimate a ~5% scatter in the inferred MW halo mass among hydrodynamics-based simulations.

79 ASTRONOMY AND ASTROPHYSICS↗

Graph-based featurization methods for classifying small molecule compounds

For over a decade, drug-induced liver injury (DILI) has posed significant drawbacks in the synthesis and development of drugs and remains a consequential concern. With finite success within the existing preclinical models, DILI is one of the main causes of drug withdrawal or termination from the market. Particularly, this withdrawal occurs during the late stages of drug development (Kullak-Ublick, 2017). Since DILI is difficult to diagnose and treat, it has become an obstacle in the drug production market that in turn affects clinicians, pharmaceutical companies, and consumers. We propose a method for learning features of DILI-positive drugs based on the graphical relationships and patterns they possess within a network of biological databases. We also train various statistical and machine learning models on these learned features in order to classify the drugs as DILI-positive or negative. Our methods include Random Forest, Neural networks, and logistic regression classification. We utilize labeled DILI-positive and DILI-negative datasets, which were developed by the FDA and the National center for toxicological research, as well as additional literature datasets (Thakkar, 2020) in order to validate our results and assess our featurization and model accuracy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

A hybrid approach to excited-state-specific variational Monte Carlo and doubly excited states

We extend our hybrid linear-method/accelerated-descent variational Monte Carlo optimization approach to excited states and investigate its efficacy in double excitations. In addition to showing a superior statistical efficiency when compared to the linear method, our tests on small molecules show good energetic agreement with benchmark methods. We also demonstrate the ability to treat double excitations in systems that are too large for a full treatment by using selected configuration interaction methods via an application to 4-aminobenzonitrile. Finally, we investigate the stability of state-specific variance optimization against collapse to other states’ variance minima and find that symmetry, Ansatz quality, and sample size all have roles to play in achieving stability.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Single crystal growth and characterization of new Zintl phase Ca 9 Zn 3.1 In 0.9 Sb 9

Complex Zintl phases have yielded a large variety of promising new thermoelectric materials. Here we report the discovery of the new Zintl phase Ca 9 (Zn 1– x In x ) 4 Sb 9 ( x ~ 0.9), needle-like crystals of which were serendipitously obtained from an In- and Sb-rich flux. Although its composition is reminiscent of Ca 9 Zn 4+ x Sb 9 , an excellent thermoelectric material with zT > 1, the substitution of In on the Zn site leads to the formation of an entirely new structure type. Single crystal X-ray diffraction revealed a structure characterized by T Sb 4 tetrahedra ( T = statistically disordered Zn and In atoms) and ZnSb 3 triangular units, which share common corners to form [ T 4 Sb 9 ] 18– polyanions. The average structure was found to have hexagonal symmetry. The valence electron count in this heavily-disordered structure appears to follows the Zintl-Klemm rules, suggesting semiconducting behavior. Single crystal electrical conductivity and Seebeck coefficient measurements support this conclusion, suggesting that the as-grown crystals are degenerate p -type semiconductors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Predictive analytics to direct clinical attention to complex patients with elevated suicide risk: enhancement of the Veterans Health Administration REACH VET model

Suicide is a major public health concern, particularly among Veterans. The U.S. Department of Veterans Affairs Veterans Health Administration (VHA) employs the Recovery Engagement and Coordination for Health–Veterans Enhanced Treatment (REACH VET) model to prioritise high-risk patients for targeted clinical attention. REACH VET 1.0 (RV 1.0) was developed on 2008–2011 data. To reflect changes in clinical practice and populations, VHA updated it to REACH VET 2.0 (RV 2.0). This study describes its development and validation. RV 2.0 used longitudinal data from 7,248,170 VHA patients (4,967 suicide deaths) in 2018–2019, with 650 time-varying demographic, clinical and area-level predictors derived from a 2-year lookback (2016–2019). An ensemble of Elastic-Net logistic regression models was trained on 2018 data and evaluated monthly at the population level in 2019, focusing on the top 0.1% intervention risk tier. Analyses assessed model discrimination, suicide detection, risk concentration, subgroup consistency (sex, age and race/ethnicity) and performance relative to RV 1.0 using the same percentile-based risk strata. RV 2.0 outperformed RV 1.0 across all risk strata, with better discrimination (C-statistic 0.76 vs 0.69) and consistent performance across demographic subgroups. Within the top 0.1% of predicted risk, RV 2.0 identified more deaths, higher suicide rates and greater mortality risk concentration both when averaged across the 12 monthly 2019 test sets (5.6 vs 3.6; 83.6 vs 53.7 per 100,000 person-years; 21.0 vs 14.1) and when annualised for 2019 (67 vs 43; 2.7% vs 1.7%; 1,003 vs 644 per 100,000 person-years; 26.7 vs 17.1). RV 2.0 improves suicide risk stratification among Veterans, demonstrating better performance and consistent prediction across subgroups and highlighting the need for regular model updates and evaluation.

Peluso, Alina [Oak Ridge National Laboratory (ORNL↗

Probabilistic Groundwater Modeling of the RDX Plume at Los Alamos National Laboratory to Support Risk Assessment - 20359

A plume of the contaminant hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) with concentrations greater than the New Mexico tap water drinking standard (9.66 ppb) is present in the regional aquifer near the southwestern boundary of Los Alamos National Laboratory (LANL). A risk assessment is performed for exposure to regional aquifer groundwater, with long-term predictions of RDX concentrations provided by a calibrated, probabilistic, numerical fate and transport model. The structure of the model is hierarchical, with the RDX Regional Aquifer (RA) groundwater model acting as the primary tool for analysis of downgradient RDX concentrations. The RA model is deeply informed by the conceptual site model (CSM) and is calibrated using site RDX concentration data and hydraulic head measurements, along with other analyses. The model is calibrated using data through December 2019. Where data are scarce other lines of evidence are used to inform inputs, including the multiphase RDX Vadose Zone (VZ) model and Pipe and Disk analytical screening tool. Model inputs are described with informative prior distributions using a robust approach to development that incorporates all available lines of evidence for every parameter used as an input to the RA model. Calibration is performed using a classical nonlinear optimization routine, which is then used to initialize a Bayesian calibration. The Bayesian calibration constrains the uncertainty in the classical calibration, ultimately providing posterior distributions for all model parameters. The challenges of the calibration include high-dimensional parameter space, including spatially heterogeneous hydraulic conductivities, comparatively sparse data, and low RDX concentrations. Posterior parameter distributions developed in the calibration process are then used for stochastic predictive model runs into the future. The result of the forward runs is spatially and temporally explicit estimates of head and concentration with uncertainty at all points in space and time. The probabilistic modeling approach presented here includes innovative computational and statistical methods that leverage high-performance computing (HPC) resources. It makes use of prior modeling work performed at the chromium plume site in the central LANL area, with extensive updates. The risk assessment will ultimately be used to support decision-making at the site, using multiple appropriately weighted sources of information, as well as uncertainty, sensitivity, and value-of-information analyses. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗