Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Statistical error”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 901 records · Page 50
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Robust Statistical Approach for Determination of Graphite Nitridation Using Bayesian Model Comparison

A better estimation of surface reaction efficiency of semiconductor-grade graphite with atomic nitrogen, as well as the calibration error are calculated using Bayesian updating based on experimental data. Compared with a conventional deterministic model, the stochastic model approach is a powerful tool in the sense that the model is capable of taking into account underlying error correlations among the data quantities. In this paper, we investigate four different stochastic models (called “stochastic system model classes” herein) corresponding to different descriptions of modeling and measurement error structures, given one deterministic physical model. These stochastic system model classes differ in the covariance matrix structure that is used in the uncertainty model to represent uncertainties associated with the physical model and experimental measurements. For each model class, Bayesian inference is used to estimate the posterior probabilities of the physical model parameters as well as of the stochastic model parameters. Model comparison and selection are then applied based on two measures including Bayesian evidence and Bayesian information criterion, as well as the deviance information criterion. Both measures suggest the stochastic model class, which considers that a correlation between errors in two data quantities among different data points is the most plausible. With the stochastic model class, the range of uncertainty in surface reaction efficiency is estimated to be about two orders of magnitude at [Formula: see text].

Engineering↗
DOE OSTI2014

SU-E-T-51: Bayesian Network Models for Radiotherapy Error Detection

Purpose: To develop a probabilistic model of radiotherapy plans using Bayesian networks that will detect potential errors in radiation delivery. Methods: Semi-structured interviews with medical physicists and other domain experts were employed to generate a set of layered nodes and arcs forming a Bayesian Network (BN) which encapsulates relevant radiotherapy concepts and their associated interdependencies. Concepts in the final network were limited to those whose parameters are represented in the institutional database at a level significant enough to develop mathematical distributions. The concept-relation knowledge base was constructed using the Web Ontology Language (OWL) and translated into Hugin Expert Bayes Network files via the the RHugin package in the R statistical programming language. A subset of de-identified data derived from a Mosaiq relational database representing 1937 unique prescription cases was processed and pre-screened for errors and then used by the Hugin implementation of the Estimation-Maximization (EM) algorithm for machine learning all parameter distributions. Individual networks were generated for each of several commonly treated anatomic regions identified by ICD-9 neoplasm categories including lung, brain, lymphoma, and female breast. Results: The resulting Bayesian networks represent a large part of the probabilistic knowledge inherent in treatment planning. By populating the networks entirely with data captured from a clinical oncology information management system over the course of several years of normal practice, we were able to create accurate probability tables with no additional time spent by experts or clinicians. These probabilistic descriptions of the treatment planning allow one to check if a treatment plan is within the normal scope of practice, given some initial set of clinical evidence and thereby detect for potential outliers to be flagged for further investigation. Conclusion: The networks developed here support the use of probabilistic models into clinical chart checking for improved detection of potential errors in RT plans.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

zPerf: A Statistical Gray-Box Approach to Performance Modeling and Extrapolation for Scientific Lossy Compression

With the scaling up of simulation-based scientific discovery on high-performance computing systems, the disparity between compute and I/O has increased, forcing domain scientists to save only a small amount of simulation data to persistent storage. This can result in the loss of essential physics fields that are needed for data analysis. While error-bounded lossy compression has made tremendous progress in bridging the gap between compute and I/O, the lack of understanding of compression performance remains a key hurdle to its wide adoption. Here, in this work, we present zPerf, a statistical gray-box performance modeling approach for scientific lossy compression. Our contributions are threefold: 1) We develop zPerf to estimate the performance of lossy compression techniques, based on in-depth understanding and statistical modeling for data features and core compression metrics; 2) We demonstrate the in-detailed implementation of zPerf using two case studies, where we derive the performance modeling for SZ and ZFP, two leading lossy compressors; 3) We evaluate the effectiveness of zPerf on real-world datasets across various domains. Based on the evaluation, we demonstrate the efficacy of the zPerf performance model; 4) We further discuss three case studies where zPerf is applied to extrapolate the compression ratio of SZ and ZFP with alternative encoding schemes as well as ZFP with an alternative transform scheme. Through the case studies, we demonstrate the potential of zPerf for exploring the design space of lossy compression, which has hardly been studied in the literature.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Model-based optimization of near-field binary-pixelated beam shapers

The optimization of components that rely on spatially dithered distributions of transparent or opaque pixels and an imaging system with far-field filtering for transmission control is demonstrated. The binary-pixel distribution can be iteratively optimized to lower an error function that takes into account the design transmission and the characteristics of the required far-field filter. Simulations using a design transmission chosen in the context of high-energy lasers show that the beam-fluence modulation at an image plane can be reduced by a factor of 2, leading to performance similar to using a non-optimized spatial-dithering algorithm with pixels of size reduced by a factor of 2 without the additional fabrication complexity or cost. The optimization process preserves the pixel distribution statistical properties. Analysis shows that the optimized pixel distribution starting from a high-noise distribution defined by a random-draw algorithm should be more resilient to fabrication errors than the optimized pixel distributions starting from a low-noise, error-diffusion algorithm, while leading to similar beamshaping performance. Furthermore, this is confirmed by experimental results obtained with various pixel distributions and induced fabrication errors.

43 PARTICLE ACCELERATORS↗
DOE OSTI2017

Information criteria for quantifying loss of reversibility in parallelized KMC

Parallel Kinetic Monte Carlo (KMC) is a potent tool to simulate stochastic particle systems efficiently. However, despite literature on quantifying domain decomposition errors of the particle system for this class of algorithms in the short and in the long time regime, no study yet explores and quantifies the loss of time-reversibility in Parallel KMC. Inspired by concepts from non-equilibrium statistical mechanics, we propose the entropy production per unit time, or entropy production rate, given in terms of an observable and a corresponding estimator, as a metric that quantifies the loss of reversibility. Typically, this is a quantity that cannot be computed explicitly for Parallel KMC, which is why we develop a posteriori estimators that have good scaling properties with respect to the size of the system. Through these estimators, we can connect the different parameters of the scheme, such as the communication time step of the parallelization, the choice of the domain decomposition, and the computational schedule, with its performance in controlling the loss of reversibility. From this point of view, the entropy production rate can be seen both as an information criterion to compare the reversibility of different parallel schemes and as a tool to diagnose reversibility issues with a particular scheme. As a demonstration, we use Sandia Lab's SPPARKS software to compare different parallelization schemes and different domain (lattice) decompositions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Two-Step Estimation in a Heteroscedastic Linear Regression Model

We study the problem of estimating a parameter in some heteroscedastic linear regression model in the case where the regressors consist of all order statistics based on the sample of identically distributed not necessarily independent observations with finite second moment. It is assumed that the random errors depend on the parameter and distributions of the corresponding regressors. We propose a two-step procedure for finding explicit asymptotically normal estimators.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Informed total-error-minimizing priors: Interpretable cosmological parameter constraints despite complex nuisance effects

While Bayesian inference techniques are standard in cosmological analyses, it is common to interpret resulting parameter constraints with a frequentist intuition. This intuition can fail, for example, when marginalizing high-dimensional parameter spaces onto subsets of parameters, because of what has come to be known as projection effects or prior volume effects. We present the method of informed total-error-minimizing (ITEM) priors to address this problem. An ITEM prior is a prior distribution on a set of nuisance parameters, such as those describing astrophysical or calibration systematics, intended to enforce the validity of a frequentist interpretation of the posterior constraints derived for a set of target parameters (e.g., cosmological parameters). Our method works as follows. For a set of plausible nuisance realizations, we generate target parameter posteriors using several different candidate priors for the nuisance parameters. We reject candidate priors that do not accomplish the minimum requirements of bias (of point estimates) and coverage (of confidence regions among a set of noisy realizations of the data) for the target parameters on one or more of the plausible nuisance realizations. Of the priors that survive this cut, we select the ITEM prior as the one that minimizes the total error of the marginalized posteriors of the target parameters. As a proof of concept, we applied our method to the density split statistics measured in Dark Energy Survey Year 1 data. We demonstrate that the ITEM priors substantially reduce prior volume effects that otherwise arise and that they allow for sharpened yet robust constraints on the parameters of interest.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Spatially Dependent Multiple Testing Under Model Misspecification, With Application to Detection of Anthropogenic Influence on Extreme Climate Events

The Weather Risk Attribution Forecast (WRAF) is a forecasting tool that uses output from global climate models to make simultaneous attribution statements about whether and how greenhouse gas emissions have contributed to extreme weather across the globe. However, in conducting a large number of simultaneous hypothesis tests, the WRAF is prone to identifying false “discoveries.” A common technique for addressing this multiple testing problem is to adjust the procedure in a way that controls the proportion of true null hypotheses that are incorrectly rejected, or the false discovery rate (FDR). Unfortunately, generic FDR procedures suffer from low power when the hypotheses are dependent, and techniques designed to account for dependence are sensitive to misspecification of the underlying statistical model. In this paper, we develop a Bayesian decision-theoretical approach for dependent multiple testing and a nonparametric hierarchical statistical model that flexibly controls false discovery and is robust to model misspecification. We illustrate the robustness of our procedure to model error with a simulation study, using a framework that accounts for generic spatial dependence and allows the practitioner to flexibly specify the decision criteria. Finally, we apply our procedure to several seasonal forecasts and discuss implementation for the WRAF workflow. Lastly, supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

STRONG LENS TIME DELAY CHALLENGE. II. RESULTS OF TDC1

We present the results of the first strong lens time delay challenge. The motivation, experimental design, and entry level challenge are described in a companion paper. This paper presents the main challenge, TDC1, which consisted of analyzing thousands of simulated light curves blindly. The observational properties of the light curves cover the range in quality obtained for current targeted efforts (e.g., COSMOGRAIL) and expected from future synoptic surveys (e.g., LSST), and include simulated systematic errors. Seven teams participated in TDC1, submitting results from 78 different method variants. After describing each method, we compute and analyze basic statistics measuring accuracy (or bias) A, goodness of fit χ{sup 2}, precision P, and success rate f. For some methods we identify outliers as an important issue. Other methods show that outliers can be controlled via visual inspection or conservative quality control. Several methods are competitive, i.e., give |A| < 0.03, P < 0.03, and χ{sup 2} < 1.5, with some of the methods already reaching sub-percent accuracy. The fraction of light curves yielding a time delay measurement is typically in the range f = 20%-40%. It depends strongly on the quality of the data: COSMOGRAIL-quality cadence and light curve lengths yield significantly higher f than does sparser sampling. Taking the results of TDC1 at face value, we estimate that LSST should provide around 400 robust time-delay measurements, each with P < 0.03 and |A| < 0.01, comparable to current lens modeling uncertainties. In terms of observing strategies, we find that A and f depend mostly on season length, while P depends mostly on cadence and campaign duration.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Certified quantum measurement of Majorana fermions

Here, we propose a quantum self-testing protocol to certify measurements of fermion parity involving Majorana fermion modes. We further show that observing a set of ideal measurement statistics implies anticommutativity of the implemented Majorana fermion parity operators, a necessary prerequisite for Majorana detection. Our protocol is robust to experimental errors. We obtain lower bounds on the fidelities of the state and measurement operators that are linear in the errors. We propose to analyze experimental outcomes in terms of a contextuality witness W , which satisfies 〈 W 〉 ≤ 3 for any classical probabilistic model of the data. A violation of the inequality witnesses quantum contextuality, and the closeness to the maximum ideal value 〈 W 〉 = 5 indicates the degree of confidence in the detection of Majorana fermions.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Helium production prediction in HTR fuel using Monte Carlo MCB system - 18713

The integrated burnup calculation code MCB deals with the complexity of burnup process, which occurs in HTR and requires application of an integrated calculation system with consideration of spatial effects of full heterogeneous reactor model with continuous energy representation of cross section and the thermo-hydraulic coupling. MCB automatically calculates helium production in all possible reaction or decay channels. The methodology of He calculation in HTR using MCB is described. It includes transmutation trajectories analysis in few batch reloading scenarios, which allows us for integrated analysis of helium production sources. The result of performed analysis is presented showing the sources of strong contribution to the helium production rate. Statistical fluctuation effects on Monte Carlo modeling of nuclear reactors as well as the uncertainty assessment are carried out, which includes the error propagation in the depletion calculations. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2015

SU-E-T-483: In Vivo Dosimetry of Conventional and Rotational Intensity Modulated Radiotherapy Using Integral Quality Monitor (IQM)

Purpose: To investigate the accuracy, sensitivity and constancy of integral quality monitor (IQM), a new system for in vivo dosimetry of conventional intensity modulated radiation therapy (IMRT) or rotational volumetric modulated arc therapy (VMAT) Methods: A beta-version IQM system was commissioned on an Elekta Infinity LINAC equipped with 160-MLCs Agility head. The stationary and rotational dosimetric constancy of IQM was evaluated, using five-field IMRT and single-or double-arc VMAT plans for prostate and head-and-neck (H&N) patients. The plans were delivered three times over three days to assess the constancy of IQM response. Picket fence (PF) fields were used to evaluate the sensitivity of detecting MLC leaf errors. A single leaf offset was intentionally introduced during delivery of various PF fields with segment apertures of 3×1, 5×1, 10×1, and 24×1cm2. Both 2mm and 5mm decrease in the field width were used. Results: Repeated IQM measurements of prostate and H&N IMRT deliveries showed 0.4 and 0.5% average standard deviation (SD) for segment-by-segment comparison and 0.1 and 0.2% for cumulative comparison. The corresponding SDs for VMAT deliveries were 6.5, 9.4% and 0.7, 1.3%, respectively. Statistical analysis indicates that the dosimetric differences detected by IQM were significant (p < 0.05) in all PF test deliveries. The largest average IQM signal response of a 2 mm leaf error was found to be 2.1% and 5.1% by a 5mm leaf error for 3×1 cm2 field size. The same error in 24×1 cm2 generates a 0.7% and 1.4% difference in the signal. Conclusion: IQM provides an effective means for real-time dosimetric verification of IMRT/ VMAT treatment delivery. For VMAT delivery, the cumulative dosimetry of IQM needs to be used in clinical practice.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

WE-H-BRC-08: Examining Credentialing Criteria and Poor Performance Indicators for IROC Houston’s Anthropomorphic Head and Neck Phantom

Purpose: To analyze the most recent results of IROC Houston’s anthropomorphic H&N phantom to determine the nature of failing irradiations and the feasibility of altering pass/fail credentialing criteria. Methods: IROC Houston’s H&N phantom, used for IMRT credentialing for NCI-sponsored clinical trials, requires that an institution’s treatment plan must agree with measurement within 7% (TLD doses) and ≥85% pixels must pass 7%/4 mm gamma analysis. 156 phantom irradiations (November 2014 – October 2015) were re-evaluated using tighter criteria: 1) 5% TLD and 5%/4 mm, 2) 5% TLD and 5%/3 mm, 3) 4% TLD and 4%/4 mm, and 4) 3% TLD and 3%/3 mm. Failure/poor performance rates were evaluated with respect to individual film and TLD performance by location in the phantom. Overall poor phantom results were characterized qualitatively as systematic (dosimetric) errors, setup errors/positional shifts, global but non-systematic errors, and errors affecting only a local region. Results: The pass rate for these phantoms using current criteria is 90%. Substituting criteria 1-4 reduces the overall pass rate to 77%, 70%, 63%, and 37%, respectively. Statistical analyses indicated the probability of noise-induced TLD failure at the 5% criterion was <0.5%. Using criteria 1, TLD results were most often the cause of failure (86% failed TLD while 61% failed film), with most failures identified in the primary PTV (77% cases). Other criteria posed similar results. Irradiations that failed from film only were overwhelmingly associated with phantom shifts/setup errors (≥80% cases). Results failing criteria 1 were primarily diagnosed as systematic: 58% of cases. 11% were setup/positioning errors, 8% were global non-systematic errors, and 22% were local errors. Conclusion: This study demonstrates that 5% TLD and 5%/4 mm gamma criteria may be both practically and theoretically achievable. Further work is necessary to diagnose and resolve dosimetric inaccuracy in these trials, particularly for systematic dose errors. This work is funded by NCI Grant CA180803.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Improving multiwell petrophysical interpretation from well logs via machine learning and statistical models

Well-log interpretation estimates in situ rock properties along well trajectory, such as porosity, water saturation, and permeability, to support reserve-volume estimation, production forecasts, and decision making in reservoir development. However, due to measurement errors, variability of well logs caused by multiple measurement vendors, different borehole tools, and nonuniform drilling/borehole conditions, estimations of rock properties with original well logs without proper preprocessing may not be accurate, especially in the context of multiwell estimation. Well-log normalization techniques such as two-point scaling and mean-variance normalization are commonly used to improve the robustness of multiwell rock-property estimation. However, these techniques do not consider the correlation between well logs and require subjective knowledge for their effective implementation. To reduce uncertainties and processing time associated with multiwell rock-property estimation from well logs, we develop discriminative adversarial (DA) and linear constraint models for well-log normalization and rock-property estimation. The DA neural network model developed for well-log normalization and interpretation can perform linear and nonlinear well-log normalization while considering the joint distribution of each well log and rock properties. However, the linear constraint model uses an ensemble of predictions from linear models to constrain well-log normalization and rock-property estimation. We also develop a divergence-based type well identification method to select type (training) wells for a test well based on the statistical similarity of associated well-log distributions instead of the interwell distance. We apply the DA model to perform well-log normalization and prediction of permeability for the Seminole San Andres Unit carbonate reservoir. Compared with the permeability predicted with the classical machine learning model without well-log normalization and models with two-point scaling normalization, the DA model yields the most accurate permeability prediction by decreasing the mean-squared error of permeability prediction by 20%–50%.

Geochemistry & Geophysics↗
DOE OSTI2016

Validation of a pretreatment delivery quality assurance method for the CyberKnife Synchrony system

Purpose: To evaluate the geometric and dosimetric accuracies of the CyberKnife Synchrony respiratory tracking system (RTS) and to validate a method for pretreatment patient-specific delivery quality assurance (DQA). Methods: An EasyCube phantom was mounted on the ExacTrac gating phantom, which can move along the superior–inferior (SI) axis of a patient to simulate a moving target. The authors compared dynamic and static measurements. For each case, a Gafchromic EBT3 film was positioned between two slabs of the EasyCube, while a PinPoint ionization chamber was placed in the appropriate space. There were three steps to their evaluation: (1) the field size, the penumbra, and the symmetry of six secondary collimators were measured along the two main orthogonal axes. Dynamic measurements with deliberately simulated errors were also taken. (2) The delivered dose distributions (from step 1) were compared with the planned ones, using the gamma analysis method. The local gamma passing rates were evaluated using three acceptance criteria: 3% local dose difference (LDD)/3 mm, 2%LDD/2 mm, and 3%LDD/1 mm. (3) The DQA plans for six clinical patients were irradiated in different dynamic conditions, to give a total of 19 cases. The measured and planned dose distributions were evaluated with the same gamma-index criteria used in step 2 and the measured chamber doses were compared with the planned mean doses in the sensitive volume of the chamber. Results: (1) A very slight enlargement of the field size and of the penumbra was observed in the SI direction (on average <1 mm), in line with the overall average CyberKnife system error for tracking treatments. (2) Comparison between the planned and the correctly delivered dose distributions confirmed the dosimetric accuracy of the RTS for simple plans. The multicriteria gamma analysis was able to detect the simulated errors, proving the robustness of their method of analysis. (3) All of the DQA clinical plans passed the tests, both in static and dynamic conditions. No statistically significant differences were found between static and dynamic cases, confirming the high degree of accuracy of the Synchrony RTS. Conclusions: The presented methods and measurements verified the mechanical and dosimetric accuracy of the Synchrony RTS. Their method confirms the fact that the RTS, if used properly, is able to treat a moving target with great precision. By combining PinPoint ion chamber, EBT3 films, and gamma evaluation of dose distributions, their DQA method robustly validated the effectiveness of CyberKnife and Synchrony system.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Hierarchical Modeling and Statistical Calibration for Photometric Redshifts

The cosmological exploitation of modern photometric galaxy surveys requires both accurate (unbiased) and precise (narrow) redshift probability distributions derived from broadband photometry. Existing methodologies do not meet these requirements. Standard template fitting delivers interpretable models and errors, but lacks the flexibility to learn inaccuracies in the observed photometry or spectral templates. Machine learning addresses those issues, but requires representative training data, and the resulting models and uncertainties cannot be interpreted in the context of a physical model or outside of the training data. We present a hierarchical modeling approach simultaneously addressing the issues of flexibility, interpretability, and generalization. It combines template fitting with flexible (machine-learning-like) models to correct the spectral templates, model their redshift distributions, and recalibrate the photometric observations. By optimizing the full posterior distribution of the model and solving for its (thousands of) parameters, one can perform a global statistical calibration of the data and the spectral energy distribution (SED) model. We apply this approach to the public Dark Energy Survey Science Verification data and show that it provides more accurate and compact redshift posterior distributions than existing methods, as well as insights into residual photometric and SED systematics. Here, the model is causal and makes predictions for future data (e.g., additional photometric bandpasses), and its internal parameters and components are interpretable. This approach does not formally require the training data to be complete or representative; in principle, it can even work in regimes in which few or no spectroscopic redshifts are available.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Coupling localized Noah-MP-Crop model with the WRF model improved dynamic crop growth simulation across Northeast China

Croplands play a critical role in regulating the energy and moisture exchanges between the land surface and atmosphere. However, the interactions between cropland and climate are usually poorly represented due to a lack of detailed representation in crop types and field management. Here, we coupled the Noah-MP-Crop model with the state-of-the-art Weather Research and Forecasting (WRF) model to explore and evaluate the crop growth dynamics in response to climate variations across Northeast China. The default parameters of the crop model were not exactly suitable for the agricultural ecosystems in Northeast China. The detailed cropland distribution, and crop phenology parameters including growing degree days (GDD) and planting (harvesting) date were first created using multi-source remote sensing products and reanalysis data, and was then successfully used to simulate the growth and yield for corn and soybean and associated energy exchanges. We also optimized and calibrated other crop parameters using the time-series of the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface products. The modified crop model substantially improved the simulation of crop growth, plant physiology, and biomass accumulation for both corn and soybean. Coupling the localized dynamic crop model into the WRF led to considerable decreases in the simulated mean-absolute-errors (MAEs) and biases of the leaf area index, evapotranspiration, and gross primary production compared with the MODIS observed values. Compared with the statistical yield from each province, the modified crop model underestimated the corn yield from 11.1% to 48.6%, whereas overestimated the soybean yield from 16.5% to 162.6%.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Microscopic theory of onset of decaging and bond-breaking activated dynamics in ultradense fluids with strong short-range attractions

We theoretically study thermally activated “in cage” elementary dynamical processes that precede full structural relaxation in ultra-dense particle liquids interacting via strong short range attractive forces. The analysis is based on a microscopic theory formulated at the particle trajectory level built on the dynamic free energy concept and an explicit treatment of how attractive forces control the formation and lifetime of physical bonds. Mean time scales for bond breaking, the early stage of cage escape, and non-Fickian displacement by a fixed amount are analyzed in the repulsive glass, bonded repulsive (attractive) glass, fluid, and dense gel regimes. The theory predicts a strong length-scale-dependent growth of these time scales with attractive force strength at fixed packing fraction, a much weaker slowing down with density at fixed attraction strength, and a strong decoupling of the shorter bond breaking time with the other two time scales that are controlled mainly by perturbed steric caging. All results are in good accord with simulations, and additional testable predictions are made. In conclusion, the classic statistical mechanical projection approximation of replacing all bare attractive and repulsive forces with a single effective force determined by pair structure incurs major errors for describing processes associated with thermally activated escape from transiently localized states.

36 MATERIALS SCIENCE↗