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At least 163 records · Page 9

Anomalous structural recovery in the near glass transition range in a polymer glass: Data revisited in light of temperature variability in vacuum oven‐based experiments*

Abstract There is considerable interest in data reported by Cangialosi et al. [Cangialosi, D., Boucher, V. M., Alegría, A., & Colmenero, J. (2013). Physical Review Letters , 111 (9), 095701] that purportedly showed unusual irregular responses in the isothermal structural recovery behavior of glasses because the observation challenges the general view of the dynamics being smooth functions of time as observed in extensive dilatometric experiments by Kovacs [Kovacs, A. J. (1964). In Fortschritte der Hochpolymeren‐Forschung , 3 , 394–507. Springer, Berlin, Heidelberg.] in the 1960s. The reported data show an apparent two mechanism structural (enthalpy) recovery at aging temperatures ranging from 6 to 17 K below which is also controversial as it could not be reproduced in work from Koh and Simon [Koh, Y. P., & Simon, S. L. (2013). Macromolecules , 46(14), 5815–5821.] where a second plateau observed in the enthalpy loss curve was not reproduced in nearly 1‐year‐long experiments at 15 K below . Therefore, it is important to determine what might be possible reasons for the apparent non‐smooth relaxations. Here, we examine the possibility that the poor temperature control of a typical vacuum oven could explain the anomalous results. We used the Tool–Narayanaswamy–Moynihan (TNM) model of structural recovery to calculate the effect of typical vacuum oven temperature variation on the structural recovery of polystyrene and find that we can reproduce the reported experimental results. The issue of temperature control using a vacuum oven is discussed, and data from thermocouple measurements of temperature at various locations inside a vacuum oven are shown.

36 MATERIALS SCIENCE↗

Sooting tendencies of co-optima test gasolines and their surrogates

Here, this study characterized the sooting tendencies of a set of gasolines and their surrogates using both experimental and computational methods. Sooting tendency was defined in terms of the soot yield when 1000 ppm of the test fuel is doped into the fuel of a methane/air flame, and it provides a measure of the intrinsic chemical tendency of the fuels to form soot in a generic combustion environment. The test fuels were real gasolines containing enhanced concentrations of alkanes, aromatics, cycloalkanes, olefins, and ethanol. These compositional differences caused the experimentally measured sooting tendencies of the fuels to vary by 240%. The surrogates were 3 mixtures defined by Szybist et al. (2017) and 3 alternative formulations modified for greater experimental convenience. The sooting tendencies measured for the surrogate mixtures agreed with the real fuels to within 15%, and varied with composition in the same order. The sooting tendencies of the surrogates could be predicted to within experimental error with an empirical quantitative structure-property relationship and a linear mixing model. The experimental flames were computationally simulated with a 743-species mechanism, and sooting tendencies derived from the results agreed with the measured values to within 11%. Altogether, these results show that the sooting behavior of gasoline can vary considerably within the range of acceptable compositions, and that these variations can be accurately predicted with empirical models and computational simulations.

09 BIOMASS FUELS↗

Evaluating Charge Equilibration Methods To Generate Electrostatic Fields in Nanoporous Materials

Charge equilibration (Qeq) methods can estimate the electrostatic potential of molecules and periodic frameworks by assigning point charges to each atom, using only a small fraction of the resources needed to compute density functional (DFT)-derived charges. This makes possible, for example, the computational screening of thousands of microporous structures to assess their performance for the adsorption of polar molecules. Recently, different variants of the original Qeq scheme were proposed to improve the quality of the computed point charges. One focus of this research was to improve the gas adsorption predictions in metal-organic frameworks (MOFs), for which many different structures are available. In this work, we review the evolution of the method from the original Qeq scheme, understanding the role of the different modifications on the final output. We evaluated the result of combining different protocols and set of parameters, by comparing the Qeq charges with high quality DFT-derived DDEC charges for 2338 MOF structures. We focused on the systematic errors that are attributable to specific atom types to quantify the final precision that one can expect from Qeq methods in the context of gas adsorption where the electrostatic potential plays a significant role, namely, CO 2 and H 2 S adsorption. In conclusion, both the type of algorithm and the input parameters have a large impact on the resulting charges, and we draw some guidelines to help the user to choose the proper combination of the two for obtaining a meaningful set of charges. We show that, considering this set of MOFs, the accuracy of the original Qeq scheme is often still comparable with the most recent variants, even if it clearly fails in the presence of certain atom types, such as alkali metals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Characterizing Performance of Freshwater Wetland Methane Models Across Time Scales at FLUXNET-CH 4 Sites Using Wavelet Analyses

Process-based land surface models are important tools for estimating global wetland methane (CH 4 ) emissions and projecting their behavior across space and time. So far there are no performance assessments of model responses to drivers at multiple time scales. In this study, we apply wavelet analysis to identify the dominant time scales contributing to model uncertainty in the frequency domain. We evaluate seven wetland models at 23 eddy covariance tower sites. Our study first characterizes site-level patterns of freshwater wetland CH 4 fluxes (FCH 4 ) at different time scales. A Monte Carlo approach was developed to incorporate flux observation error to avoid misidentification of the time scales that dominate model error. Our results suggest that (a) significant model-observation disagreements are mainly at multi-day time scales (<15 days); (b) most of the models can capture the CH 4 variability at monthly and seasonal time scales (>32 days) for the boreal and Arctic tundra wetland sites but have significant bias in variability at seasonal time scales for temperate and tropical/subtropical sites; (c) model errors exhibit increasing power spectrum as time scale increases, indicating that biases at time scales <5 days could contribute to persistent systematic biases on longer time scales; and (d) differences in error pattern are related to model structure (e.g., proxy of CH 4 production). Our evaluation suggests the need to accurately replicate FCH 4 variability, especially at short time scales, in future wetland CH 4 model developments.

58 GEOSCIENCES↗

On the choice of reference orbitals for linear-response calculations of solution-phase K-edge X-ray absorption spectra

The simplest response theory methods for computing vertical excitation spectra in condensed-phase are configuration interaction with single excitations (CIS) and linear-response time-dependent density functional theory (TDDFT) within the Tamm–Dancoff approximation. In applications to X-ray absorption spectroscopy (XAS), methods like CIS and TDDFT that codify only single excitations into the wave function are prone to catastrophic errors in main-edge and post-edge features whose shapes act as a crucial fingerprint in structural analyses of liquids. Here, we show that these errors manifest primarily due to a lack of orbital relaxation in conventional linear-response theories and that core-ionized (n - 1-electron) references, like those of electron-affinity TDDFT, can eliminate the errors in the spectral profile, even in the highest-energy parts of the post-edge. Crucially, we find that single excitations atop core-ionized references are sufficient to elucidate liquid-phase XAS spectra with semi-quantitative accuracy, opening the door for methods like electron-affinity CIS/TDDFT to be used as efficient alternatives to higher-order wave function approaches.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Shockwave dissipation by interface-dominated porous structures

The advent of additive manufacturing (AM) has enabled topological control of structures at the micrometer scale, transforming the properties of polymers for a variety of applications. Examples include tailored mechanical responses, acoustic properties, and thermal properties. Porous polymer materials are a class of materials used for shock and blast mitigation, yet they frequently possess a lack of structural order and are largely developed and evaluated via trial-and-error. Here, we demonstrate control of shockwave dissipation through interface-dominated structures prepared by AM using 2-photon polymerization. A fractal structure with voids, or free surfaces, arranged less than 100 μm apart, allows for rarefaction interactions on the timescale of the shockwave loading. Simulations and dynamic x-ray phase contrast imaging experiments show that fractal structures with interfaces assembled within a “critical” volume reduce shockwave stress and wave velocity by over an order of magnitude within the first unit cell.

36 MATERIALS SCIENCE↗

Bond length alternation of π -conjugated polymers predicted by the Fermi–Löwdin orbital self-interaction correction method

π-conjugated polymers have been used in a wide range of practical applications, partly due to their unique properties that originate in the delocalization of electrons through the polymer backbone. The level of delocalization can be characterized by the induced bond length alternation (BLA), with shorter BLA connected with strong delocalization and vice versa. The accurate description of this structural parameter can be considered a benchmark for testing the capability of different electronic structure methods for self-interaction error (SIE) removal and electron correlation inclusion. Density functional theory (DFT), in its local or semi-local flavors, suffers from SIE and, thus, underestimates the BLA compared to self-interaction-free methods. In this work, we utilize the Fermi–Löwdin orbital self-interaction correction (FLOSIC) method for one-electron self-interaction removal to characterize the BLA of five oligomers with increasing length extrapolated to the polymeric limit. We compare the self-interaction-free BLA to several DFT approximations, Møller–Plesset second-order perturbation theory (MP2), and the BLA obtained with the domain based local pair natural orbital CCSD(T) [DLPNO-CCSD(T)] approximation. Our findings show that FLOSIC corrects for the small BLA given by (semi-)local DFT approximations, but it tends to overcorrect with respect to CAM-B3LYP, MP2, and DLPNO-CCSD(T).

Chemistry↗

Characterizing Sub-Cohorts via Data Normalization and Representation Learning

The process of identifying a cohort of interest is a very challenging task. It requires manually inspecting many patient records of complex structure that might include medical coding errors and missing data. This paper presents a computational pipeline for refining the process of cohort selection based on medical concepts recorded in the electronic health records (EHRs). The pipeline extracts EHR data for a given cohort and normalizes this data using standard vocabularies. Then a stacked denoising autoencoder is used to embed the normalized patient vectors in a low dimensional space, where the patients are subsequently clustered into sub-cohorts. The goal is to represent the cohort in a standard format and abstract variants of sub-populations. As a use-case, we applied the pipeline to 1.8 million Veterans diagnosed with major depressive disorder (MDD), and identified four meaningful sub-cohorts using the features learned by the autoencoder. Then, each sub-cohort was explored using a set of keywords for interpretation.

Rush III, Everett↗

TU-H-CAMPUS-JeP3-02: Automated Dose Accumulation and Dose Accuracy Assessment for Online Or Offline Adaptive Replanning

Purpose: With introduction of high-quality treatment imaging during radiation therapy (RT) delivery, e.g., MR-Linac, adaptive replanning of either online or offline becomes appealing. Dose accumulation of delivered fractions, a prerequisite for the adaptive replanning, can be cumbersome and inaccurate. The purpose of this work is to develop an automated process to accumulate daily doses and to assess the dose accumulation accuracy voxel-by-voxel for adaptive replanning. Methods: The process includes the following main steps: 1) reconstructing daily dose for each delivered fraction with a treatment planning system (Monaco, Elekta) based on the daily images using machine delivery log file and considering patient repositioning if applicable, 2) overlaying the daily dose to the planning image based on deformable image registering (DIR) (ADMIRE, Elekta), 3) assessing voxel dose deformation accuracy based on deformation field using predetermined criteria, and 4) outputting accumulated dose and dose-accuracy volume histograms and parameters. Daily CTs acquired using a CT-on-rails during routine CT-guided RT for sample patients with head and neck and prostate cancers were used to test the process. Results: Daily and accumulated doses (dose-volume histograms, etc) along with their accuracies (dose-accuracy volume histogram) can be robustly generated using the proposed process. The test data for a head and neck cancer case shows that the gross tumor volume decreased by 20% towards the end of treatment course, and the parotid gland mean dose increased by 10%. Such information would trigger adaptive replanning for the subsequent fractions. The voxel-based accuracy in the accumulated dose showed that errors in accumulated dose near rigid structures were small. Conclusion: A procedure as well as necessary tools to automatically accumulate daily dose and assess dose accumulation accuracy is developed and is useful for adaptive replanning. Partially supported by Elekta, Inc.

60 APPLIED LIFE SCIENCES↗

An Autonomous MCP Bridge to Rucio: Enhancing Data Management Accessibility for High Energy Physics

The Rucio Data Management System [1] is an important tool used by High Energy Physics experiments, including those at Fermi National Accelerator Laboratory, to store and manage exabyte-scale scientific datasets. Despite its central role in coordinating data across globally distributed storage sites, Rucio's command line interface (CLI) presents a steep learning curve, and makes it difficult for scientists to navigate through. To solve this issue, a containerized Model Context Protocol (MCP) [2] server was built that connects Large Language Models directly to Rucio, allowing AI agents to handle data tasks by using simple, natural language rather than memorized terminal commands. The core engineering focus of this project was moving the server away from slow terminal commands that require text parsing and replacing them with a native Python Client API toolset and a planned REST API framework. Moving to the Python API handles data operations directly in memory, which helps clear up formatting errors, provides the AI with clean, structured JSON data and speeds up tool execution. To prove that the system actually works, a benchmarking pipeline was also built with various questions to test the AI across four different model configurations. The questions included finding data scopes, tracking down specific datasets, and checking replication rules. Through benchmarking, early runs showed that with raw terminal text, the model would get confused and stuck, whereas switching to the Python API to feed the AI clean, structured data yielded massive improvement. By creating an intelligent and autonomous bridge to a storage network, this project shows how AI can be implemented in scientific data management, which ultimately helps scientists at Fermilab spend less time sorting through data and more time focusing on their experiments and analysis.

Akella, Kashyap [William Rainey Harper Coll.]↗

A non-intrusive optical (NIO) approach to characterize heliostats in utility-scale power tower plants: Sensitivity study

Power towers, a type of concentrating power tower technology, use a large number of heliostats to concentrate sunlight and produce renewable energy. Optical errors of heliostats can cause drastic losses in the efficiency of power-tower plants. Accurately measuring optical errors is crucial to assessing and improving plant performance. This analysis discusses an innovate non-intrusive optical (NIO) approach to measure slope, canting, and tracking errors by detecting distortions in the reflected tower structure in heliostat images. Further in this work a sensitivity study is carefully conducted to determine the uncertainty requirements to allow the method to calculate slope errors with an accuracy of 0.25 mrad. Measurement uncertainty sources include camera resolution and position uncertainty, tower position uncertainty, and number of collected images. Each uncertainty source is investigated to determine its impact on the accuracy of the slope-error calculation. A combination of theoretical results and experimental results obtained from data collected on a heliostat at Sandia National Laboratories is used to determine and validate uncertainty requirements. The analysis shows that a measurement uncertainty of 0.25 mrad can be realized by realistically controlling uncertainty sources when implementing the NIO method. It demonstrates the superior performance of NIO in performing in-situ optical characterization.

14 SOLAR ENERGY↗

Structural basis for DNA proofreading

DNA polymerase (DNAP) can correct errors in DNA during replication by proofreading, a process critical for cell viability. However, the mechanism by which an erroneously incorporated base translocates from the polymerase to the exonuclease site and the corrected DNA terminus returns has remained elusive. Here, we present an ensemble of nine high-resolution structures representing human mitochondrial DNA polymerase Gamma, Polγ, captured during consecutive proofreading steps. The structures reveal key events, including mismatched base recognition, its dissociation from the polymerase site, forward translocation of DNAP, alterations in DNA trajectory, repositioning and refolding of elements for primer separation, DNAP backtracking, and displacement of the mismatched base into the exonuclease site. Altogether, our findings suggest a conserved ‘bolt-action’ mechanism of proofreading based on iterative cycles of DNAP translocation without dissociation from the DNA, facilitating primer transfer between catalytic sites. Functional assays and mutagenesis corroborate this mechanism, connecting pathogenic mutations to crucial structural elements in proofreading steps.

59 BASIC BIOLOGICAL SCIENCES↗

Scalable line and plane relaxation in a parallel structured multigrid solver

The efficient solution of sparse, linear systems that arise through the discretization of partial differential equations remains a key challenge for a range of high performance scientific simulations. One approach for reducing data movement and improving performance is by exposing and exploiting structure in a problem through the use of robust structured multilevel solvers. By choosing coarsening that preserves the structure of the problem, these methods maintain efficient structured computation and communication throughout the multigrid hierarchy. However, when coarsening is not permitted to be dependent on the operator, anisotropy must be addressed by the smoother — producing error compatible for coarse-grid correction with structured coarsening. Here, the components required in a scalable parallel structured solver are described with a focus on memory and communication efficiency of robust smoothers. While the implementation of communication and memory reduction techniques in smoothers integrated in a complete 3D solver present a significant engineering challenge, a novel approach is proposed that addresses these challenges systematically through a change to the solver’s execution model. Enabled by user-level threading paired with a set of data and communication abstractions, this approach permits seamless aggregation of communication in plane smoothers — directly reusing code for a 2D distributed multilevel cycle. Results show an effective reduction in communication costs for coarse-grid problems, and result in a speedup of 8.7x in smoothing routines shown in Fig. 12 using this approach. This produces a significant improvement to strong scalability while maintaining favorable weak scaling behavior. Finally, a parallel scaling study using a series of refined meshes is included that demonstrates the effectiveness of this approach in an application of interest.

97 MATHEMATICS AND COMPUTING↗

Experimental and theoretical study of energy transfer in a chromophore triad: What makes modeling dynamics successful?

Simulation of electronic dynamics in realistically large molecular systems is a demanding task that has not yet achieved the same level of quantitative prediction already realized for its static counterpart. This is particularly true for processes occurring beyond the Born–Oppenheimer regime. Non-adiabatic molecular dynamics (NAMD) simulations suffer from two convoluted sources of error: numerical algorithms for dynamics and electronic structure calculations. While the former has gained increasing attention, particularly addressing the validity of ad hoc methodologies, the effect of the latter remains relatively unexplored. Indeed, the required accuracy for electronic structure calculations to reach quantitative agreement with experiment in dynamics may be even more strict than that required for static simulations. In this study, we address this issue by modeling the electronic energy transfer in a donor–acceptor–donor (D–A–D) molecular light harvesting system using fewest switches surface hopping NAMD simulations. In the studied system, time-resolved experimental measurements deliver complete information on spectra and energy transfer rates. Subsequent modeling shows that the calculated electronic transition energies are “sufficiently good” to reproduce experimental spectra but produce over an order of magnitude error in simulated dynamical rates. We further perform simulations using artificially shifted energy gaps to investigate the complex relationship between transition energies and modeled dynamics to understand factors affecting non-radiative relaxation and energy transfer rates.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Global and local curvature in density functional theory

Piecewise linearity of the energy with respect to fractional electron removal or addition is a requirement of an electronic structure method that necessitates the presence of a derivative discontinuity at integer electron occupation. Semi-local exchange-correlation (xc) approximations within density functional theory (DFT) fail to reproduce this behavior, giving rise to deviations from linearity with a convex global curvature that is evidence of many-electron, self-interaction error and electron delocalization. Popular functional tuning strategies focus on reproducing piecewise linearity, especially to improve predictions of optical properties. In a divergent approach, Hubbard U-augmented DFT (i.e., DFT+U) treats self-interaction errors by reducing the local curvature of the energy with respect to electron removal or addition from one localized subshell to the surrounding system. Although it has been suggested that DFT+U should simultaneously alleviate global and local curvature in the atomic limit, no detailed study on real systems has been carried out to probe the validity of this statement. In this work, we show when DFT+U should minimize deviations from linearity and demonstrate that a “+U” correction will never worsen the deviation from linearity of the underlying xc approximation. However, we explain varying degrees of efficiency of the approach over 27 octahedral transition metal complexes with respect to transition metal (Sc–Cu) and ligand strength (CO, NH{sub 3}, and H{sub 2}O) and investigate select pathological cases where the delocalization error is invisible to DFT+U within an atomic projection framework. Finally, we demonstrate that the global and local curvatures represent different quantities that show opposing behavior with increasing ligand field strength, and we identify where these two may still coincide.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Curiosity driven exploration to optimize structure–property learning in microscopy

Rapidly determining structure–property correlations in materials is an important challenge in better understanding fundamental mechanisms and greatly assists in materials design. In microscopy, imaging data provides a direct measurement of the local structure, while spectroscopic measurements provide relevant functional property information. Deep kernel active learning approaches have been utilized to rapidly map local structure to functional properties in microscopy experiments, but are computationally expensive for multi-dimensional and correlated output spaces. Here, we present an alternative lightweight curiosity algorithm which actively samples regions with unexplored structure–property relations, utilizing a deep-learning based surrogate model for error prediction. We show that the algorithm outperforms random sampling for predicting properties from structures, and provides a convenient tool for efficient mapping of structure–property relationships in materials science.

36 MATERIALS SCIENCE↗

Regression of exchangeable relational arrays

Relational arrays represent measures of association between pairs of actors, often in varied contexts or over time. Trade flows between countries, financial transactions between individuals, contact frequencies between school children in classrooms and dynamic protein-protein interactions are all examples of relational arrays. Elements of a relational array are often modelled as a linear function of observable covariates. Uncertainty estimates for regression coefficient estimators, and ideally the coefficient estimators themselves, must account for dependence between elements of the array, e.g., relations involving the same actor. Existing estimators of standard errors that recognize such relational dependence rely on estimating extremely complex, heterogeneous structure across actors. This paper develops a new class of parsimonious coefficient and standard error estimators for regressions of relational arrays. Here we leverage an exchangeability assumption to derive standard error estimators that pool information across actors, and are substantially more accurate than existing estimators in a variety of settings. This exchangeability assumption is pervasive in network and array models in the statistics literature, but not previously considered when adjusting for dependence in a regression setting with relational data. We demonstrate improvements in inference theoretically, via a simulation study, and by analysis of a dataset involving international trade.

97 MATHEMATICS AND COMPUTING↗

Multiscale Persistent Functions for Biomolecular Structure Characterization

Here in this paper, we introduce multiscale persistent functions for biomolecular structure characterization. The essential idea is to combine our multiscale rigidity functions (MRFs) with persistent homology analysis, so as to construct a series of multiscale persistent functions, particularly multiscale persistent entropies, for structure characterization. To clarify the fundamental idea of our method, the multiscale persistent entropy (MPE) model is discussed in great detail. Mathematically, unlike the previous persistent entropy (Chintakunta et al. in Pattern Recognit 48(2):391–401, 2015; Merelli et al. in Entropy 17(10):6872–6892, 2015; Rucco et al. in: Proceedings of ECCS 2014, Springer, pp 117–128, 2016), a special resolution parameter is incorporated into our model. Various scales can be achieved by tuning its value. Physically, our MPE can be used in conformational entropy evaluation. More specifically, it is found that our method incorporates in it a natural classification scheme. This is achieved through a density filtration of an MRF built from angular distributions. To further validate our model, a systematical comparison with the traditional entropy evaluation model is done. Additionally, it is found that our model is able to preserve the intrinsic topological features of biomolecular data much better than traditional approaches, particularly for resolutions in the intermediate range. Moreover, by comparing with traditional entropies from various grid sizes, bond angle-based methods and a persistent homology-based support vector machine method (Cang et al. in Mol Based Math Biol 3:140–162, 2015), we find that our MPE method gives the best results in terms of average true positive rate in a classic protein structure classification test. More interestingly, all-alpha and all-beta protein classes can be clearly separated from each other with zero error only in our model. Finally, a special protein structure index (PSI) is proposed, for the first time, to describe the “regularity” of protein structures. Basically, a protein structure is deemed as regular if it has a consistent and orderly configuration. Our PSI model is tested on a database of 110 proteins; we find that structures with larger portions of loops and intrinsically disorder regions are always associated with larger PSI, meaning an irregular configuration, while proteins with larger portions of secondary structures, i.e., alpha-helix or beta-sheet, have smaller PSI. Essentially, PSI can be used to describe the “regularity” information in any systems.

59 BASIC BIOLOGICAL SCIENCES↗