Study of the Decoherence Correction Derived from the Exact Factorization Approach for Nonadiabatic Dynamics
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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
SEARCH · Engineering Papers
Results for “Mathematical physics methods”
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.
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Multiscale Strategy for Predicting Radiation Chemistry in Polymers
A primary mode for radiation damage in polymers arises from ballistic electrons that induce electronic excitations, yet subsequent chemical mechanisms are poorly understood. We develop a multiscale strategy to predict this chemistry starting from subatomic scattering calculations. Nonadiabatic molecular dynamics simulations sample initial bond-breaking events following the most likely excitations, which feed into semiempirical simulations that approach chemical equilibrium. Application to polyethylene reveals a mechanism explaining the low propensity to cross-link in crystalline samples.
36 MATERIALS SCIENCE↗
Hybrid Auxiliary Field Quantum Monte Carlo for Molecular Systems
Not Available
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Understanding the Interplay between the Nonvalence and Valence States of the Uracil Anion upon Monohydration
In this work we present an ab initio investigation into the effect of monohydration on the interaction of uracil with low energy electrons. Electron attachment and photodetachment experimental studies have previously shown dramatic changes in uracil upon solvation with even a single water molecule, due to an inversion of the character of the ground state of the anion. Here we explore the interplay between the nonvalence and valence states of the uracil anion, as a function of geometry and site of solvation. Our model provides unambiguous interpretation of previous photoelectron studies, reproducing the binding energies and photoelectron images for bare uracil and a single isomer of the U•(H 2 O) 1 cluster. Furthermore, the results of this study provide insight into how electrons may attach to hydrated nucleobases. These results lay the foundations for further investigations into the effect of microhydration on the electronic structure and electron capture dynamics of nucleobases.
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3D-Scaffold: A Deep Learning Framework to Generate 3D Coordinates of Drug-like Molecules with Desired Scaffolds
The prerequisite of therapeutic drug design is to identify novel molecules with desired biophysical and biochemical properties. Deep generative models have demonstrated their ability to find such molecules by exploring a huge chemical space efficiently. An effective way to obtain molecules with desired target properties is the preservation of critical scaffolds in the generation process. To this end, we propose a domain aware generative framework called 3D-Scaffold that takes 3D coordinates of a desired scaffold as an input and generates 3D coordinates of novel therapeutic candidates as an output while always preserving the desired scaffolds in generated structures. We show that our framework generates predominantly valid, unique, novel, and experimentally synthesizable molecules that have drug-like properties similar to the molecules in the training set. Using domain specific datasets, we generate covalent and non-covalent antiviral inhibitors. Therefore, to measure the success of our framework in generating therapeutic candidates, generated structures were subjected to high throughput virtual screening via docking simulations, which shows favorable interaction against SARS-CoV-2 main protease and non-structural protein endoribonuclease (NSP15) targets. Most importantly, our model performs well with relatively small volumes of training data and generalizes to new scaffolds, making it applicable to other domain.
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Spectral Signatures of Ultrafast Excited-State Intramolecular Proton Transfer from Computational Multi-edge Transient X-ray Absorption Spectroscopy
Excited state intramolecular proton transfer (ESIPT) is a fundamental chemical process with several applications. Ultrafast ESIPT involves coupled electronic and atomic motions and has been primarily studied using femtosecond optical spectroscopy. X-ray spectroscopy is particularly useful because it is element-specic and enables direct, individual probes of the proton donating and accepting atoms. Here, we report a computational study to resolve the ESIPT in 10-Hydroxybenzo[h]quinoline (HBQ), an intramolecularly hydrogen bonded compound, using time-dependent density functional theory combined with ab initio molecular dynamics (AIMD) and time-resolved X-ray absorption spectroscopy (XAS) computations to track the ultrafast excited-state dynamics. Overall, our results reveal clear X-ray spectral signatures of coupled electronic and atomic motions during and following ESIPT at the oxygen and nitrogen K-edge paving the way for future experiments at X-ray free electron lasers.
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Anomalies and Local Structure of Liquid Water from Boiling to the Supercooled Regime as Predicted by the Many-Body MB-pol Model
For the past 50 years, researchers have sought molecular models that can accurately reproduce water’s microscopic structure and thermophysical properties across broad ranges of its complex phase diagram. Herein, molecular dynamics simulations with the many-body MB-pol model are performed to monitor the thermodynamic response functions and local structure of liquid water from the boiling point down to deeply supercooled temperatures at ambient pressure. The isothermal compressibility and isobaric heat capacity show maxima near 223 K, in excellent agreement with recent experiments, and the liquid density exhibits a minimum at ~208 K. A local tetrahedral arrangement, where each water molecule accepts and donates two hydrogen bonds, is found to be the most probable hydrogen-bonding topology at all temperatures. Further, this work suggests that MB-pol may provide predictive capability for studies of liquid water’s physical properties across broad ranges of thermodynamic states, including the so-called water’s “no man’s land” which is difficult to probe experimentally.
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Hydrogen Bonding and Its Effect on the Orientational Dynamics of Water Molecules inside Polyelectrolyte Brush-Induced Soft and Active Nanoconfinement
Despite being the most ubiquitous compound on Earth, the fundamental properties of water are not fully understood, especially in nanoconfinement. Densely grafted polyelectrolyte (PE) molecules attain the configuration of a “brush”: these PE brushes, due to their ability to form hydrogen bonds (HBs) with water via the PE functional groups, act as a source of soft and active nanoconfinement for the brush-trapped water molecules. In this paper, we study the effects of PE brush-induced confinement on the structure, dynamics and energetics of the water-water and water-PE HBs. Our results indicate a significant weakening of the HBs from bulk to sparsely grafted to densely grafted brushes. i.e., by increasing the degree of brush-induced nanoconfinement. We explain that this weakening of water-water HBs is caused by the disruption of the extended network of water molecules within the brush-induced nanoconfinement. This is confirmed by performing a ring structure analysis of the water molecules, which yields a reduction in the average ring size at higher degrees of brush-induced nanoconfinement (i.e., at higher brush grafting densities). Furthermore, we investigate the role of HB on the orientational dynamics of the water molecules. Here, we observe that the rotational motion of the water molecules becomes sluggish inside the PE brushes. Recent findings have indicated that the water and counterions trapped in brush-induced nanoconfinement demonstrate structures (in combination with the PE functional group) analogous to that in “water-in-salt” electrolytes that have seen extensive recent uses for Li-ion battery applications. However, the rotational dynamics of water molecules inside the brush-induced nanoconfinement is found to be distinctly different from that of conventional “water-in-salt” electrolytes in absence of any confinement; therefore, the present study will provide the necessary platform towards conceptualizing polymer-based nanoconfinement for battery applications.
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Formation of the N≡N Triple Bond from Reductive Coupling of a Paramagnetic Diruthenium Nitrido Compound
Construction of nitrogen–nitrogen triple bonds via homocoupling of metal nitrides is an important fundamental reaction relevant to a potential Nitrogen Economy. Here, we report that room temperature photolysis of Ru 2 (chp) 4 N 3 (chp– = 2-chloro-6-hydroxypyridinate) in CH 2 Cl 2 produces N 2 via reductive coupling of Ru2(chp)4N nitrido species. Computational analysis reveals that the nitride coupling transition state (TS) features an out-of-plane “zigzag” geometry instead of the anticipated planar zigzag TS. However, with intentional exclusion of dispersion correction, the planar zigzag TS geometry can also be found. Both the out-of-plane and planar zigzag TS geometries feature two important types of orbital interactions: (1) donor–acceptor interactions involving intermolecular donation of a nitride lone pair into an empty Ru–N π* orbital and (2) Ru–N π to Ru–N π* interactions derived from coupling of nitridyl radicals. The relative importance of these two interactions is quantified both at and after the TS. Our analysis shows that both interactions are important for the formation of the N–N σ bond, while radical coupling interactions dominate the formation of N–N π bonds. Comparison is made to isoelectronic Ru 2 -oxo compounds. Formation of an O–O bond via bimolecular oxo coupling is not observed experimentally and is calculated to have a much higher TS energy. As a result, the major difference between the nitrido and oxo systems stems from an extremely large driving force, ~–500 kJ/mol, for N–N coupling vs a more modest driving force for O–O coupling, –40 to –140 kJ/mol.
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Selected Uses of TSUNAMI in Critical Experiment Design and Analysis
Validation in criticality safety is performed by comparing the results of critical experiments with the calculated results from models of the experiments using the computational method to be validated. Laboratory critical experiments are controlled systems that achieve a k eff of approximately 1 and enable investigation of the parameters at which such a critical condition is achieved. For the critical experiments used in a validation to capture the biases of the materials and neutron energy spectra of interest, those materials must be included in the experiment such that they influence k eff or another observable parameter with statistical significance. This paper discusses the use of sensitivity uncertainty (S/U) methods to develop critical experiments for various purposes. S/U techniques are useful for understanding the underlying components of nuclear data which affect the k eff or another parameter of a given configuration. S/U calculations are most commonly used to compare existing experiments to applications of interest; however, S/U techniques can also be used to identify, optimize, or assess features of proposed experiments so that they can better test specific portions of nuclear data or match an application of interest. The S/U techniques discussed here are from the TSUNAMI code system. The two primary codes discussed in this work are TSUNAMI-3D, which implements the KENO criticality code to calculate the sensitivity of k eff to nuclear data, and TSAR, which calculates the sensitivity of a reactivity difference between two configurations based on their TSUNAMI-3D generated sensitivity profiles. The methods used in these tools are discussed in more detail in the SCALE manual. This paper is one of a series on the development and use of TSUNAMI tools. The other papers address development of TSUNAMI methods and a review of TSUNAMI applications.
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A Review of TSUNAMI Applications
The TSUNAMI-1D and TSUNAMI-3D sequences for generating k eff sensitivity coefficients were first released in SCALE 5 in 2004. Several other tools were introduced in the same release, including primarily the TSUNAMI-IP code for uncertainty analysis and similarity assessments. The TSUNAMI sequences added capabilities for identifying important reactions and data for systems using sensitivity coefficients and also introduced new quantitative tools for rigorously assessing the similarity of systems. Over time, the capabilities of these tools have grown, and their use has expanded into other areas such as nuclear data assessment and nuclear covariance data testing. This paper discusses applications of the TSUNAMI tools for sensitivity calculations, sensitivity/uncertainty (S/U)-based validation, and nuclear data testing. This paper is one of a series of papers at this conference on the development and use of TSUNAMI tools. The other papers address the development of the TSUNAMI methods and the use of TSUNAMI for critical experiment design and optimization.
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Exploring Applied Cryptosystems to Formally Verify Security in Cyber-Physical Systems
This project aims to evaluate RSA as a method for public-key encryption for cyber-physical systems (CPS). As technology advances, cyber attacks are increasing, and with them, the need for cybersecurity advances; the average cost for cybercrime in the world was estimated at $6 trillion in 2021. A public-key cryptosystem that has been around since 1977, RSA has recently garnered some critiques for its fragility, computational cost, and lazy implementation. In this project I will review the mathematical derivation of RSA, analyze the practical implications of such mathematical framework for the security of RSA, and propose a formal methods based approach to verify encryption schemes for CPS.
97 MATHEMATICS AND COMPUTING↗
Optimal design of acoustic metamaterial cloaks under uncertainty
In this work, we consider the problem of optimal design of an acoustic cloak under uncertainty and develop scalable approximation and optimization methods to solve this problem. The design variable is taken as an infinite-dimensional spatially-varying field that represents the material property, while an additive infinite-dimensional random field represents the variability of the material property or the manufacturing error. Discretization of this optimal design problem results in high-dimensional design variables and uncertain parameters. To solve this problem, we develop a computational approach based on a Taylor approximation and an approximate Newton method for optimization, which is based on a Hessian derived at the mean of the random field. We show our approach is scalable with respect to the dimension of both the design variables and uncertain parameters, in the sense that the necessary number of acoustic wave propagations is essentially independent of these dimensions, for numerical experiments with up to one million design variables and half a million uncertain parameters. Additionally, we demonstrate that, using our computational approach, an optimal design of the acoustic cloak that is robust to material uncertainty is achieved in a tractable manner. The optimal design under uncertainty problem is posed and solved for the classical circular obstacle surrounded by a ring-shaped cloaking region, subjected to both a single-direction single-frequency incident wave and multiple-direction multiple-frequency incident waves. Finally, we apply the method to a deterministic large-scale optimal cloaking problem with complex geometry, to demonstrate that the approximate Newton method’s Hessian computation is viable for large, complex problems.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Impact of Increased Latent Generations on Sensitivity Calculations with SCALE [Slides]
Depending on the system, energy, and/or nuclide, altering the number of latent generations can significantly impact generated sensitivity coefficients. In general, as the number of latent generations increases, the accuracy of the generated sensitivity value compared with the DP value increases and there is a significant increase in the uncertainty with generated sensitivity values. IFP generated sensitivities appear to be more stable than those with CLUTCH. Complex systems, even with IFP and increased latent generations can still produce poor results (i.e., MPC- 32). This reiterates the importance of performing DPs to confirm sensitivities. IFP-Shift and CLUTCH can take advantage of parallel computing abilities. Work continues in this area as additional parameters and calculational methods are examined to provide analysts insights to successfully generating sensitivity coefficients for confirmatory analyses and validation efforts.
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Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons
Neural networks (NNs) are currently changing the computational paradigm on how to combine data with mathematical laws in physics and engineering in a profound way, tackling challenging inverse and ill-posed problems not solvable with traditional methods. However, quantifying errors and uncertainties in NN-based inference is more complicated than in traditional methods. This is because in addition to aleatoric uncertainty associated with noisy data, there is also uncertainty due to limited data, but also due to NN hyperparameters, overparametrization, optimization and sampling errors as well as model misspecification. Although there are some recent works on uncertainty quantification (UQ) in NNs, there is no systematic investigation of suitable methods towards quantifying the total uncertainty effectively and efficiently even for function approximation, and there is even less work on solving partial differential equations and learning operator mappings between infinite-dimensional function spaces using NNs. In this work, we present a comprehensive framework that includes uncertainty modeling, new and existing solution methods, as well as evaluation metrics and post-hoc improvement approaches. Further, to demonstrate the applicability and reliability of our framework, we present an extensive comparative study in which various methods are tested on prototype problems, including problems with mixed input-output data, and stochastic problems in high dimensions. In the Appendix, we include a comprehensive description of all the UQ methods employed. Further, to help facilitate the deployment of UQ in Scientific Machine Learning research and practice, we present and develop in [1] an open-source Python library (github.com/Crunch-UQ4MI/neuraluq), termed NeuralUQ, that is accompanied by an educational tutorial and additional computational experiments.
11 physics-informed neural networks↗
A consistent, Bayesian, approach to the cross section probability distribution in the unresolved resonance region
The cross sections of neutron-induced reactions can be divided into three energy ranges: the resolved resonance region (RRR), the unresolved resonance region (URR), and the fast region. In general, the cross sections in the URR show significant fluctuations that cannot be predicted and cannot be experimentally resolved, thus, it is commonly assumed that the cross section at a specific energy is given by a probability distribution function (PDF) over a range of values that can span several orders of magnitude. The current methodology used to describe such behavior is to construct the PDF by stochastically generating resonance ladders and numerically measuring the PDF. The resonance ladders are sampled using known resonance statistical properties and average resonance widths and spacings extrapolated from the RRR. Although this is a standard and widely used technique, it is computationally very expensive, therefore, an alternative, analytical, approach would be preferable due to the considerable speed up of the computational time in real life applications. Moreover, the current methodology does not take into account existing experimental data, such for total and capture cross sections, that are available for many nuclei. Finally, this approach was developed to be used in reactor-scale applications and it is not suited for use in single-event applications. In this work we will rethink the entire approach to the PDF construction using a Bayesian mindset. This will allow us to provide a different definition of the PDF that allows a much faster calculation of the higher-temperature PDFs and a proper combination of theoretical and experimental PDFs following the probability theory. We will also show that our definition is well suited for single-event applications and we will make an explicit connection between our method and the standard approach. We do this by showing that the central limit theorem applies and our method leads to the same PDF obtained with the standard methodology, for a large number of events per history.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Analysis of SCALE Criticality and Sensitivity Calculations for Reflected HEU Cylinders [Slides]
The SCALE code package offers multiple nuclear data libraries supporting Monte Carlo transport, with sensitivity and uncertainty methods derived from MC transport solutions. Several libraries are multigroup, which introduce bias differing by system. Previous work has shown poor S/U results in several reflector materials: ICSBEP benchmark HMF-084 was selected to analyze biases and S/U method applicability to a variety of reflectors. Prior and ongoing work found inaccuracies in CSAS and TSUNAMI results, which were further investigated utilizing the HEU-MET-FAST-084 ICSBEP critical benchmark, chosen for its geometrical simplicity and variety of reflector materials. Perturbation of reflector thickness across various reflector materials allowed for an assortment of materials is to be tested swiftly for each sequence and method. Observation was an increasing bias of MG $k_{eff}$ relative to CE, in both direction and magnitude. IFP produced extremely reliable results. >85% of CLUTCH cases were found satisfactory.
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Sensitivity/Uncertainty Comparison Study Involving IRSN, LANL, and ORNL Tools to Support Validation
Under a DOE Nuclear Criticality Safety Program (NCSP) task involving Analytical Methods, three Laboratories collaborated in a comparison of results obtained from Sensitivity/Uncertainty (S/U) packages relevant to validation of transport codes. The task involves Institut de Radioprotection et de Sûreté Nucléaire (IRSN), Los Alamos National Laboratory (LANL), and Oak Ridge National Laboratory (ORNL) comparing results of MORET 5/MACSENS V3.0, MCNP6.2/Whisper-1.1, and SCALE 6.2.3/TSUNAMI/USLSTATS respectively. All Monte Carlo transport code results utilize nuclear data from ENDF/B-VII.1 evaluation. This study examines five cases from the International Handbook of Evaluated Criticality Safety Benchmark Experiments (ICSBEP Handbook) selected as application models: IEU-MET- FAST-002-001, LEU-COMP-THERM-001-001, LEU-SOL-THERM-004-001, MIX-COMP- THERM-001-001, and U233-SOL-THERM-001-001. This is a continuation of a previous study to examine Pu and HEU cases: HEU-MET-FAST-013-001, HEU-SOL-THERM-001-008, PU-MET- FAST-022-001, and PU-SOL-THERM-001-001. Ultimately, comparison is made between Upper Subcritical Limits (USLs) obtained using each code package for each application case. Since differences exist in whether packages take into account margin of subcriticality (MOS), the USL is computed using only bias and bias uncertainty, also known as the calculational margin (CM) in ANSI/ANS-8.24. Results comparison appears to show that benchmark selection has a greater influence on the USL than the method used for calculation of bias and bias uncertainty.
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