LLNL Analytical Methods Update [Slides]
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A new method for generating locally orthogonal polygonal meshes from a set of generator points is presented in which polygon areas are a constraint. The area constraint property is particularly useful for particle methods where moving polygons track a discrete portion of material. Because Voronoi polygon meshes have some very attractive mathematical and numerical properties for numerical computation, a generalization of Voronoi polygon meshes was formulated that enforces a polygon area constraint. Area constrained moving polygonal meshes allow one to develop hybrid particle-mesh numerical methods that display some of the most attractive features of each approach. It is shown that this mesh construction method can continuously reconnect a moving, unstructured polygonal mesh in a pseudo-Lagrangian fashion without change in cell area/volume, and the method's ability to simulate various physical scenarios is shown. Overall, the advantages are identified for incompressible fluid flow calculations, with demonstration cases that include material discontinuities of all three phases of matter and large density jumps.
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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.
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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.
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.
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.
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.
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.
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.
A general method for systematizing a bulk of data on the secondary-electron spectrum into a small number of indices is shown. The method is based on thorough examination of the mathematical physics that governs the spectrum. Several examples of the analysis of recent experimental data on proton impact are presented.
Computational mechanics is that discipline of applied science and engineering devoted to the study of physical phenomena by means of computational methods based on mathematical modeling and simulation, utilizing digital computers. The discipline combines theoretical and applied mechanics, approximation theory, numerical analysis, and computer science. Computational mechanics has had a major impact on engineering analysis and design. When applied to structural mechanics, the discipline is referred to herein as computational structural mechanics. Complex structures being considered by NASA for the 1990's include composite primary aircraft structures and the space station. These structures will be much more difficult to analyze than today's structures and necessitate a major upgrade in computerized structural analysis technology. NASA has initiated a research activity in structural analysis called Computational Structural Mechanics (CSM). The broad objective of the CSM activity is to develop advanced structural analysis technology that will exploit modern and emerging computers, such as those with vector and/or parallel processing capabilities. Here, the current research directions for the Methods and Application Studies Team of the Langley CSM activity are described.
The Institute for Problems in Mechanics' Laboratory work in mathematical and physical modelling of fluid mechanics develops models, methods, and software for analysis of fluid flow, instability analysis, direct numerical modelling and semi-empirical models of turbulence, as well as experimental research and verification of these models and their applications in technological fluid dynamics, microgravity fluid mechanics, geophysics, and a number of engineering problems. This paper presents an overview of the results in microgravity fluid dynamics research during the last two years. Nonlinear problems of weakly compressible and compressible fluid flows are discussed.
Gravity-driven thermosolutal convection that arises during controlled directional solidification (DS) of dendritic alloys promotes detrimental macro-segregation (e.g. freckles and steepling) in products such as turbine blades. Considerable time and effort has been spent to experimentally and theoretically investigate this phenomena; although our knowledge has advanced to the point where convection can be modeled and accurately compared to experimental results, little has been done to minimize its onset and deleterious effects. The experimental work demonstrates that segregation can be. minimized and microstructural uniformity promoted when a slow axial rotation is applied to the sample crucible during controlled directional solidification processing. Numerical modeling utilizing continuation and bifurcation methods have been employed to develop accurate physical and mathematical models with the intent of identifying and optimizing processing parameters.
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.
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.
A reliability analysis studies a mathematical model of a physical system taking into account uncertainties of design variables and common results are estimations of a response density, which also implies estimations of its parameters. Some common density parameters include the mean value, the standard deviation, and specific percentile(s) of the response, which are measures of central tendency, variation, and probability regions, respectively. Reliability analyses are important since the results can lead to different designs by calculating the probability of observing safe responses in each of the proposed designs. All of this is done at the expense of added computational time as compared to a single deterministic analysis which will result in one value of the response out of many that make up the density of the response. Sampling methods, such as monte carlo (MC) and latin hypercube sampling (LHS), can be used to perform reliability analyses and can compute nonlinear response density parameters even if the response is dependent on many random variables. Hence, both methods are very robust; however, they are computationally expensive to use in the estimation of the response density parameters. Both methods are 2 of 13 stochastic methods that are contained within the Numerical Evaluation of Stochastic Structures Under Stress (NESSUS) program. NESSUS is a probabilistic finite element analysis (FEA) program that was developed through funding from NASA Glenn Research Center (GRC). It has the additional capability of being linked to other analysis programs; therefore, probabilistic fluid dynamics, fracture mechanics, and heat transfer are only a few of what is possible with this software. The LHS method is the newest addition to the stochastic methods within NESSUS. Part of this work was to enhance NESSUS with the LHS method. The new LHS module is complete, has been successfully integrated with NESSUS, and been used to study four different test cases that have been proposed by the Society of Automotive Engineers (SAE). The test cases compare different probabilistic methods within NESSUS because it is important that a user can have confidence that estimates of stochastic parameters of a response will be within an acceptable error limit. For each response, the mean, standard deviation, and 0.99 percentile, are repeatedly estimated which allows confidence statements to be made for each parameter estimated, and for each method. Thus, the ability of several stochastic methods to efficiently and accurately estimate density parameters is compared using four valid test cases. While all of the reliability methods used performed quite well, for the new LHS module within NESSUS it was found that it had a lower estimation error than MC when they were used to estimate the mean, standard deviation, and 0.99 percentile of the four different stochastic responses. Also, LHS required a smaller amount of calculations to obtain low error answers with a high amount of confidence than MC. It can therefore be stated that NESSUS is an important reliability tool that has a variety of sound probabilistic methods a user can employ and the newest LHS module is a valuable new enhancement of the program.