A priori evaluation of aqueous polarization effects through Monte Carlo QM-MM simulations

Science ◽  
1992 ◽  
Vol 258 (5082) ◽  
pp. 631-635 ◽  
Author(s):  
J Gao ◽  
X Xia
Keyword(s):  
Monte Carlo ◽  
A Priori ◽  
Polarization Effects

scholarly journals COSMIC COVARIANCE AND THE LOW QUADRUPOLE ANISOTROPY IN THE WMAP DATA

2008 ◽  
Vol 23 (17n20) ◽  
pp. 1489-1497 ◽  
Author(s):  
LUNG-YIH CHIANG ◽  
PAVEL D. NASELSKY ◽  
PETER COLES
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
A Priori ◽  
Gaussian Random Field ◽  
Minimum Variance ◽  
Microwave Background ◽  
Data Release ◽  
Intrinsic Correlation ◽  
Wmap Data ◽  
Cmb Temperature

Low quadrupole power in the cosmic microwave background (CMB) temperature anisotropies has been a puzzle since WMAP data release. In this talk I will demonstrate that the minimum variance optimization (MVO), a methodology used by many authors including the WMAP science team to separate the CMB from foreground contamination, serves not only to extract the CMB, but to subtract the “cosmic covariance”, an intrinsic correlation between the CMB and the foregrounds. Such subtraction induces low variance in the signal via MVO, which in turn propagates into the multipoles, causing a quadrupole deficit with more than 90% CL. As we do not know the CMB and the foregrounds a priori, and their correlation is subtracted by the MVO in any case, there is therefore an unknown error in the quadrupole power even before the cosmic variance interpretation. We combine the MVO and Monte Carlo simulations, assuming CMB is a Gaussian random field, and the estimated quadrupole power falls in [308.13, 401.97] μ K 2 (at 1 − σ level).

A Backward Monte Carlo method for fast-ion-loss simulations

2021 ◽  
Author(s):  
Filippo Zonta ◽  
Lucia Sanchis ◽  
Eero Hirvijoki
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
A Priori ◽  
Target Surface ◽  
A Priori Information ◽  
Ion Distributions ◽  
Plasma Facing Components ◽  
Priori Information ◽  
Fast Ion

Abstract This paper presents a novel scheme to improve the statistics of simulated fast-ion loss signals and power loads to plasma-facing components in fusion devices. With the so-called Backward Monte Carlo method, the probabilities of marker particles reaching a chosen target surface can be approximately traced from the target back into the plasma. Utilizing the probabilities as {\it a priori} information for the well-established Forward Monte Carlo method, statistics in fast-ion simulations are significantly improved. For testing purposes, the scheme has been implemented to the ASCOT suite of codes and applied to a realistic ASDEX Upgrade configuration of beam-ion distributions.

Triple-Defect B2 Binary Intermetallics: Bragg-Williams Solution and Monte Carlo Simulations

2009 ◽  
Vol 289-292 ◽  
pp. 361-368 ◽  
Author(s):  
Andrzej Biborski ◽  
L. Zosiak ◽  
Rafal Abdank-Kozubski
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Vacancy Concentration ◽  
A Priori ◽  
Equilibrium Concentration ◽  
Pair Interaction ◽  
Type Model ◽  
Interaction Energies ◽  
Atomic Pair ◽  
Antisite Defects

Surprisingly low rate of “order-order” kinetics in stoichiometric NiAl intermetallic known of very high vacancy concentration suggested a specific triple-defect mechanism of ordering/disordering in this system [1]. This mechanism implies a correlation between the concentrations of antisite defects and vacancies; the latters being trapped in triple defects and thus, inactive as atomic migration agents. The process was modelled by means of Monte Carlo (MC) simulations recognised as a powerful tool for such tasks [2], but requiring now the implementation of thermal vacancy thermodynamics. Temperature dependence of vacancy concentration in an AB B2 binary system was determined within an Ising-type model solved first in Bragg-Williams approximation [3] and then by means of MC simulation of a Grandcanonical Ensemble. Without any a priori assumptions concerning the formation of particular types of point defects the model yielded temperature domains where the concentrations of antisite defects and vacancies were proportional. The effect associated with the formation of triple defects appeared for specific values of atomic pair-interaction energies. Moreover, non-stoichiometric A-B systems with the same atomic pair-interaction energies showed the existence of constitutional vacancies at low temperatures. Monte Carlo simulations of “order-order” (disordering) kinetics in B2 AB systems modelled with triple-defect-promoting atomic pair-interaction energies were run with temperature-dependent concentra-tion (i.e. number) of vacancies given by the above model. The simulated relaxations showed two stages: (i) rapid formation of triple defects engaging almost all vacancies present in the system, (ii) very slow process of further generation of antisite defects until the equilibrium concentration was reached. The result reproduced very well the experimental observations [1].

L1 Regularization-Based Model Reduction of Complex Chemistry Molecular Dynamics for Statistical Learning of Kinetic Monte Carlo Models

MRS Advances ◽  
2016 ◽  
Vol 1 (24) ◽  
pp. 1767-1772 ◽  
Author(s):  
Qian Yang ◽  
Carlos A. Sing-Long ◽  
Evan J. Reed
Keyword(s):  
Molecular Dynamics ◽  
Monte Carlo ◽  
Kinetic Monte Carlo ◽  
A Priori ◽  
Reaction Network ◽  
Md Simulations ◽  
Rate Coefficients ◽  
Elementary Reactions ◽  
L1 Regularization ◽  
Complex Chemistry

ABSTRACTKinetic Monte Carlo (KMC) methods have been a successful technique for accelerating time scales and increasing system sizes beyond those achievable with fully atomistic simulations. However, a requirement for its success is a priori knowledge of all relevant reaction pathways and their rate coefficients. This can be difficult for systems with complex chemistry, such as shock-compressed materials at high temperatures and pressures or phenolic spacecraft heat shields undergoing pyrolysis, which can consist of hundreds of molecular species and thousands of distinct reactions. In this work, we develop a method for first estimating a KMC model composed of elementary reactions and rate coefficients by using large datasets derived from a few molecular dynamics (MD) simulations of shock compressed liquid methane, and then using L1 regularization to reduce the estimated chemical reaction network. We find that the full network of 2613 reactions can be reduced by 89% while incurring approximately 9% error in the dominant species (CH4) population. We find that the degree of sparsity achievable decreases when similar accuracy is required for additional populations of species.

Hamiltonian Monte Carlo algorithms for target- and interval-oriented amplitude versus angle inversions

Geophysics ◽  
2020 ◽  
Vol 85 (3) ◽  
pp. R177-R194 ◽  
Author(s):  
Mattia Aleardi ◽  
Alessandro Salusti
Keyword(s):  
Monte Carlo ◽  
A Priori ◽  
Computational Cost ◽  
Model Space ◽  
Time Interval ◽  
Long Distance ◽  
Hamiltonian Monte Carlo ◽  
Zoeppritz Equations ◽  
Monte Carlo Algorithms ◽  
Amplitude Versus

A reliable assessment of the posterior uncertainties is a crucial aspect of any amplitude versus angle (AVA) inversion due to the severe ill-conditioning of this inverse problem. To accomplish this task, numerical Markov chain Monte Carlo algorithms are usually used when the forward operator is nonlinear. The downside of these algorithms is the considerable number of samples needed to attain stable posterior estimations especially in high-dimensional spaces. To overcome this issue, we assessed the suitability of Hamiltonian Monte Carlo (HMC) algorithm for nonlinear target- and interval-oriented AVA inversions for the estimation of elastic properties and associated uncertainties from prestack seismic data. The target-oriented approach inverts the AVA responses of the target reflection by adopting the nonlinear Zoeppritz equations, whereas the interval-oriented method inverts the seismic amplitudes along a time interval using a 1D convolutional forward model still based on the Zoeppritz equations. HMC uses an artificial Hamiltonian system in which a model is viewed as a particle moving along a trajectory in an extended space. In this context, the inclusion of the derivative information of the misfit function makes possible long-distance moves with a high probability of acceptance from the current position toward a new independent model. In our application, we adopt a simple Gaussian a priori distribution that allows for an analytical inclusion of geostatistical constraints into the inversion framework, and we also develop a strategy that replaces the numerical computation of the Jacobian with a matrix operator analytically derived from a linearization of the Zoeppritz equations. Synthetic and field data inversions demonstrate that the HMC is a very promising approach for Bayesian AVA inversion that guarantees an efficient sampling of the model space and retrieves reliable estimations and accurate uncertainty quantifications with an affordable computational cost.

Estimation and Use of the Radiation Distribution Factor Median for Predicting Uncertainty in the Monte Carlo Ray-Trace Method

2019 ◽  
Vol 141 (6) ◽  
Author(s):  
Mehran Yarahmadi ◽  
J. Robert Mahan ◽  
Kory J. Priestley
Keyword(s):  
Heat Transfer ◽  
Monte Carlo ◽  
A Priori ◽  
Heat Transfer Analysis ◽  
Distribution Factor ◽  
Recent Contribution ◽  
Radiation Distribution ◽  
Radiation Behavior ◽  
Ray Trace ◽  
Probability Density Function Pdf

In a recent contribution, the authors show that the uncertainty in heat transfer results obtained using the Monte Carlo ray-trace (MCRT) method is related to the median of the radiation distribution factor probability density function (PDF). The value of this discovery would be significantly enhanced if the median could be known a priori without first computing the distribution factors. This would allow the user to determine the number of rays required to achieve the desired accuracy of a subsequent heat transfer analysis. The current contribution presents a correlation for the median of the distribution factor PDF as a function of emissivity and the number of surface elements defining an enclosure. The correlation involves a single parameter whose value is unique for a given enclosure geometry. We find that the radiation behavior of a given enclosure can be classified on a scale ranging from reflection-dominated to geometry-dominated. The correlation is shown to work well for reflection-dominated enclosures but less well for geometry-dominated enclosures.

Sign in / Sign up

Export Citation Format

Share Document