Journal articles: 'Kinetic Monte Carlo Methods'

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Srivastava, Argala, K. P. Singh, and S. B. Degweker. "Monte Carlo Methods for Reactor Kinetic Simulations." Nuclear Science and Engineering 189, no. 2 (November 14, 2017): 152–70. http://dx.doi.org/10.1080/00295639.2017.1388091.

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Herty, M., A. Klar, and L. Pareschi. "General Kinetic Models for Vehicular Traffic Flows and Monte-Carlo Methods." Computational Methods in Applied Mathematics 5, no. 2 (2005): 155–69. http://dx.doi.org/10.2478/cmam-2005-0008.

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AbstractIn this paper we present a general derivation of kinetic models for traffic flows including different kinds of interaction rules. We show that most kinetic previously derived models can be cast in the actual formulation. The development of Monte-Carlo methods for direct simulation of kinetic models is considered as an initial step towards realistic and effcient computations of traffic phenomena. Monte-Carlo methods are developed for these kinetic models. Several numerical examples are computed and compared to the previously obtained solutions of the stationary equation.

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Xiaopeng Xu, Xiaopeng Xu, Chuancai Liu Xiaopeng Xu, Hongji Yang Chuancai Liu, and Xiaochun Zhang Hongji Yang. "A Multi-Trajectory Monte Carlo Sampler." 網際網路技術學刊 23, no. 5 (September 2022): 1117–28. http://dx.doi.org/10.53106/160792642022092305020.

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<p>Markov Chain Monte Carlo techniques based on Hamiltonian dynamics can sample the first or last principal components of multivariate probability models using simulated trajectories. However, when components’ scales span orders of magnitude, these approaches may be unable of accessing all components adequately. While it is possible to reconcile the first and last components by alternating between two different types of trajectories, the sampling of intermediate components may be imprecise. In this paper, a function generalizing the kinetic energies of Hamiltonian Monte Carlo and Riemannian Manifold Hamiltonian Monte Carlo is proposed, and it is found that the methods based on a specific form of the function can more accurately sample normal distributions. Additionally, the multi-particle algorithm’s reasoning is given after a review of some statistical ideas.</p> <p> </p>

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Takano, Hiroshi. "On Monte Carlo Methods for the Kinetic Ising Model." Journal of the Physical Society of Japan 62, no. 1 (January 15, 1993): 370–71. http://dx.doi.org/10.1143/jpsj.62.370.

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Khrushcheva, O., E. E. Zhurkin, L. Malerba, C. S. Becquart, C. Domain, and M. Hou. "Copper precipitation in iron: a comparison between metropolis Monte Carlo and lattice kinetic Monte Carlo methods." Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms 202 (April 2003): 68–75. http://dx.doi.org/10.1016/s0168-583x(02)01830-x.

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Kozubski, Rafal, Graeme E. Murch, and Irina V. Belova. "Vacancy-Mediated Diffusion and Diffusion-Controlled Processes in Ordered Binary Intermetallics by Kinetic Monte Carlo Simulations." Diffusion Foundations 29 (April 2021): 95–115. http://dx.doi.org/10.4028/www.scientific.net/df.29.95.

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We review the results of our Monte Carlo simulation studies carried out within the past two decades in the area of atomic-migration-controlled phenomena in intermetallic compounds. The review aims at showing the high potential of Monte Carlo methods in modelling both the equilibrium states of the systems and the kinetics of the running processes. We focus on three particular problems: (i) the atomistic origin of the complexity of the ‘order-order’ relaxations in γ’-Ni3Al; (ii) surface-induced ordering phenomena in γ-FePt and (iii) ‘order—order’ kinetics and self-diffusion in the ‘triple-defect’ β-NiAl. The latter investigation demonstrated how diverse Monte Carlo techniques may be used to model the phenomena where equilibrium thermodynamics interplays and competes with kinetic effects.

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Kaiser, Waldemar, Manuel Gößwein, and Alessio Gagliardi. "Acceleration scheme for particle transport in kinetic Monte Carlo methods." Journal of Chemical Physics 152, no. 17 (May 7, 2020): 174106. http://dx.doi.org/10.1063/5.0002289.

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Carrillo, José Antonio, and Mattia Zanella. "Monte Carlo gPC Methods for Diffusive Kinetic Flocking Models with Uncertainties." Vietnam Journal of Mathematics 47, no. 4 (November 5, 2019): 931–54. http://dx.doi.org/10.1007/s10013-019-00374-2.

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Abstract In this paper we introduce and discuss numerical schemes for the approximation of kinetic equations for flocking behavior with phase transitions that incorporate uncertain quantities. This class of schemes here considered make use of a Monte Carlo approach in the phase space coupled with a stochastic Galerkin expansion in the random space. The proposed methods naturally preserve the positivity of the statistical moments of the solution and are capable to achieve high accuracy in the random space. Several tests on a kinetic alignment model with self propulsion validate the proposed methods both in the homogeneous and inhomogeneous setting, shading light on the influence of uncertainties in phase transition phenomena driven by noise such as their smoothing and confidence band.

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Koblents, Eugenia, Inés P. Mariño, and Joaquín Míguez. "Bayesian Computation Methods for Inference in Stochastic Kinetic Models." Complexity 2019 (January 20, 2019): 1–15. http://dx.doi.org/10.1155/2019/7160934.

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In this paper we investigate Monte Carlo methods for the approximation of the posterior probability distributions in stochastic kinetic models (SKMs). SKMs are multivariate Markov jump processes that model the interactions among species in biological systems according to a set of usually unknown parameters. The tracking of the species populations together with the estimation of the interaction parameters is a Bayesian inference problem for which Markov chain Monte Carlo (MCMC) methods have been a typical computational tool. Specifically, the particle MCMC (pMCMC) method has been shown to be effective, while computationally demanding method applicable to this problem. Recently, it has been shown that an alternative approach to Bayesian computation, namely, the class of adaptive importance samplers, may be more efficient than classical MCMC-like schemes, at least for certain applications. For example, the nonlinear population Monte Carlo (NPMC) algorithm has yielded promising results with a low dimensional SKM (the classical predator-prey model). In this paper we explore the application of both pMCMC and NPMC to analyze complex autoregulatory feedback networks modelled by SKMs. We demonstrate numerically how the populations of the relevant species in the network can be tracked and their interaction rates estimated, even in scenarios with partial observations. NPMC schemes attain an appealing trade-off between accuracy and computational cost that can make them advantageous in many practical applications.

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Hehr, Brian D. "Analysis of Radiation Effects in Silicon Using Kinetic Monte Carlo Methods." IEEE Transactions on Nuclear Science 61, no. 6 (December 2014): 2847–54. http://dx.doi.org/10.1109/tns.2014.2368075.

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Sun, Yi. "Simulations of bi-direction pedestrian flow using kinetic Monte Carlo methods." Physica A: Statistical Mechanics and its Applications 524 (June 2019): 519–31. http://dx.doi.org/10.1016/j.physa.2019.04.081.

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Chatterjee, Abhijit, and Dionisios G. Vlachos. "An overview of spatial microscopic and accelerated kinetic Monte Carlo methods." Journal of Computer-Aided Materials Design 14, no. 2 (February 28, 2007): 253–308. http://dx.doi.org/10.1007/s10820-006-9042-9.

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Bruno, D., M. Capitelli, and S. Longo. "Direct Monte Carlo Methods in Nonequilibrium Kinetics." Contributions to Plasma Physics 44, no. 56 (September 2004): 485–91. http://dx.doi.org/10.1002/ctpp.200410068.

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Bystritskaya, Elena V., Oleg N. Karpukhin, and Alla V. Kutsenova. "Monte Carlo Simulation of Linear Polymer Thermal Depolymerization under Isothermal and Dynamic Modes." International Journal of Polymer Science 2011 (2011): 1–6. http://dx.doi.org/10.1155/2011/849370.

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Kinetics of linear polymer thermal depolymerization under isothermal and dynamic TGA modes was simulated by the Monte Carlo method. The simulation was carried out on model arrays having the same initial degree of polymerization and different width (polydispersity index, ) at three constant temperatures and five heating rates. Kinetics of the process in both modes is described by the Avrami equation, the exponent in which decreasing as the distribution width increases. Treatment of the model kinetic curves of degradation using the nonlinear regression method by the Avrami equation, under both isothermal and dynamic modes, gives correct activation energy and pre-exponential factor values independently of the initial PDI. Data obtained in the dynamic mode were also treated by two isoconversion methods, widely applied to kinetic analysis of TGA curves (Flynn-Wall-Ozawa method and Kissinger-Akahira-Sunose (KAS) method).

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Zdziarski, Andrzej A., Michał Szanecki, Juri Poutanen, Marek Gierliński, and Paweł Biernacki. "Spectral and temporal properties of Compton scattering by mildly relativistic thermal electrons." Monthly Notices of the Royal Astronomical Society 492, no. 4 (January 17, 2020): 5234–46. http://dx.doi.org/10.1093/mnras/staa159.

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ABSTRACT We have obtained new solutions and methods for the process of thermal Comptonization. We modify the solution to the kinetic equation of Sunyaev and Titarchuk to allow its application up to mildly relativistic electron temperatures and optical depths $\gtrsim {1}$. The solution can be used for spectral fitting of X-ray spectra from astrophysical sources. We also have developed an accurate Monte Carlo method for calculating spectra and timing properties of thermal Comptonization sources. The accuracy of our kinetic equation solution is verified by comparison with the Monte Carlo results. We also compare our results with those of other publicly available methods. Furthermore, based on our Monte Carlo code, we present distributions of the photon emission times and the evolution of the average photon energy for both up and down scattering.

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Lin, Yen Ting, and William S. Hlavacek. "Scaling methods for accelerating kinetic Monte Carlo simulations of chemical reaction networks." Journal of Chemical Physics 150, no. 24 (June 28, 2019): 244101. http://dx.doi.org/10.1063/1.5096774.

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Caturla, María J. "Object kinetic Monte Carlo methods applied to modeling radiation effects in materials." Computational Materials Science 156 (January 2019): 452–59. http://dx.doi.org/10.1016/j.commatsci.2018.05.024.

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de Oliveira, Luís Pereira, Jan J. Verstraete, and Max Kolb. "Molecule-based kinetic modeling by Monte Carlo methods for heavy petroleum conversion." Science China Chemistry 56, no. 11 (October 16, 2013): 1608–22. http://dx.doi.org/10.1007/s11426-013-4989-3.

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LAM, CHI-HANG, and M. T. LUNG. "GREEN'S FUNCTION AND SUPER-PARTICLE METHODS FOR KINETIC SIMULATION OF HETEROEPITAXY." International Journal of Modern Physics B 21, no. 23n24 (September 30, 2007): 4219–24. http://dx.doi.org/10.1142/s0217979207045438.

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Arrays of nanosized three dimensional islands are known to self-assemble spontaneously on strained heteroepitaxial thin films. We simulate the dynamics using kinetic Monte Carlo method based on a ball and spring lattice model. Green's function and super-particle methods which greatly enhance the computational efficiency are explained.

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Zhang, Ruiqing. "Analysis of mathematical methods and principles of molecular dynamics and monte carlo method." Theoretical and Natural Science 5, no. 1 (May 25, 2023): 395–401. http://dx.doi.org/10.54254/2753-8818/5/20230252.

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In this era, molecular dynamics and Monte Carlo methods have become the primary simulation methods. With the emergence of computer simulation research methods, it has been possible to solve the sizeable computational volume and some other problems in the simulation process. At present, molecular dynamics simulations have taken an essential place in the substantial computational system and have a remarkable ability to solve multi-body problems. Therefore, researchers widely use it in many fields such as physics, chemistry, and materials science. Meanwhile, the Monte Carlo method is also a very effective statistical simulation method. This method can far surpass ordinary integration in efficiency with guaranteed computational accuracy. Furthermore, the derived kinetic Monte Carlo method can simulate and study dynamics problems. It can be seen that both simulation methods play an vital role in various disciplines. Therefore, it is very significant to understand the mathematical principles behind them and to know their advantages and disadvantages.

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Luo, Run, Xinyu Wei, and Fuyu Zhao. "ICONE23-1068 CALCULATION OF KINETIC PARAMETERS FOR ADS CORES WITH MINOR ACTINIDES BY USING MONTE CARLO METHOD." Proceedings of the International Conference on Nuclear Engineering (ICONE) 2015.23 (2015): _ICONE23–1—_ICONE23–1. http://dx.doi.org/10.1299/jsmeicone.2015.23._icone23-1_36.

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Trigilio, A. D., Y. W. Marien, M. Edeleva, P. H. M. Van Steenberge, and D. R. D'hooge. "Optimal search methods for selecting distributed species in Gillespie-based kinetic Monte Carlo." Computers & Chemical Engineering 158 (February 2022): 107580. http://dx.doi.org/10.1016/j.compchemeng.2021.107580.

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Ruiz Barlett, V., J. J. Bigeón, M. Hoyuelos, and H. O. Mártin. "Differences between fixed time step and kinetic Monte Carlo methods for biased diffusion." Journal of Computational Physics 228, no. 16 (September 2009): 5740–48. http://dx.doi.org/10.1016/j.jcp.2009.04.035.

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PAWLIK, GRZEGORZ, ANTONI C. MITUS, ANDRZEJ MINIEWICZ, and FRANCOIS KAJZAR. "MONTE CARLO SIMULATIONS OF TEMPERATURE DEPENDENCE OF THE KINETICS OF DIFFRACTION GRATINGS FORMATION IN A POLYMER MATRIX CONTAINING AZOBENZENE CHROMOPHORES." Journal of Nonlinear Optical Physics & Materials 13, no. 03n04 (December 2004): 481–89. http://dx.doi.org/10.1142/s0218863504001992.

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A simple kinetic model of temperature-dependent processes accompanying the writing and decay of diffraction gratings in a polymer matrix doped with azo-dye chromophores under illumination with spatially modulated and linearly polarized light is introduced and studied using Monte Carlo simulations. The model generalizes a recently proposed athermal kinetic model based on three-channel photoizomerization cycles of trans–cis type of azobenzene molecules and orientational redistribution and diffusion. These processes are influenced by a polymer matrix in a thermal equilibrium. The Monte Carlo simulations show the important role of temperature on the kinetic processes during building and erasure of diffraction gratings. We discuss some interesting applications for photonic devices, related to all-optical processing and storage of information using holographic methods.

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Kato, Chinami, Hiroki Nagakura, and Taiki Morinaga. "Neutrino Transport with the Monte Carlo Method. II. Quantum Kinetic Equations." Astrophysical Journal Supplement Series 257, no. 2 (December 1, 2021): 55. http://dx.doi.org/10.3847/1538-4365/ac2aa4.

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Abstract Neutrinos have a unique quantum feature as flavor conversions. Recent studies suggested that collective neutrino oscillations play important roles in high-energy astrophysical phenomena. The quantum kinetic equation (QKE) is capable of describing the neutrino flavor conversion, transport, and matter collision self-consistently. However, we have experienced many technical difficulties in their numerical implementation. In this paper, we present a new QKE solver based on a Monte Carlo (MC) approach. This is an upgraded version of our classical MC neutrino transport solver; in essence, a flavor degree of freedom including mixing state is added into each MC particle. This extension requires updating numerical treatments of collision terms, in particular for scattering processes. We deal with the technical problem by generating a new MC particle at each scattering event. To reduce statistical noise inherent in MC methods, we develop the effective mean free path method. This suppresses a sudden change of flavor state due to collisions without increasing the number of MC particles. We present a suite of code tests to validate these new modules with comparison to the results reported in previous studies. Our QKE-MC solver is developed with fundamentally different philosophy and design from other deterministic and mesh methods, suggesting that it will be complementary to others and potentially provide new insights into physical processes of neutrino dynamics.

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Carta, M., S. Dulla, V. Peluso, P. Ravetto, and G. Bianchini. "Calculation of the Effective Delayed Neutron Fraction by Deterministic and Monte Carlo Methods." Science and Technology of Nuclear Installations 2011 (2011): 1–8. http://dx.doi.org/10.1155/2011/584256.

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The studies on Accelerator-Driven Systems (ADSs) have renewed the interest in the theoretical and computational evaluation of the main integral parameters characterizing subcritical systems (e.g., reactivity, effective delayed neutron fractionβeff, and mean prompt neutron generation time). In particular, some kinetic parameters, as the effective delayed neutron fraction, are evaluated in Monte Carlo codes by formulations which do not require the calculation of the adjoint flux. This paper is focused on a theoretical and computational analysis about how the differentβeffdefinitions are connected and which are the approximations inherent to the Monte Carlo definition with respect to the standard definition involving weighted integrals. By means of a refined transport computational analysis carried out in a coherent and consistent way, that is, using the same deterministic code and neutron data library for theβeffevaluation in different ways, the theoretical analysis is numerically confirmed. Both theoretical and numerical results confirm the effectiveness of the Monte Carloβeffevaluation, at least in cases where spectral differences between total and prompt fluxes are negligible with respect to the value of the functionals entering the classicalβeffformulation.

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Guo, Xiaoyu, Xiaotong Shang, Jing Song, Guanlin Shi, Shanfang Huang, and Kan Wang. "Kinetic methods in Monte Carlo code RMC and its implementation to C5G7-TD benchmark." Annals of Nuclear Energy 151 (February 2021): 107864. http://dx.doi.org/10.1016/j.anucene.2020.107864.

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Pasichna, V. M., N. V. Storozhuk, and A. M. Gusak. "Incubation Time at Decomposition of Solid Solution – Stochastic Kinetic Mean-Field Versus Monte Carlo Simulation." Ukrainian Journal of Physics 65, no. 6 (June 9, 2020): 488. http://dx.doi.org/10.15407/ujpe65.6.488.

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The comparison of two simulation techniques applied to the nucleation in a supersaturated solid solution is made. The first one is the well-known Monte Carlo (MC) method. The second one is a recently developed modification of the atomistic self-consistent non-linear mean-field method with the additionally introduced noise of local fluxes: Stochastic Kinetic Mean-Field (SKMF) method. The amplitude of noise is a tuning parameter of the SKMF method in its comparison with the Monte Carlo one. The results of two methods for the concentration and temperature dependences of the incubation period become close, if one extrapolates the SKMF data to a certain magnitude of the noise amplitude. The results of both methods are compared also with the Classical Nucleation Theory (CNT).

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Gus'kov, S. Yu, D. V. Il'in, A. A. Levkovsky, V. B. Rozanov, V. E. Sherman, and O. B. Vygovsky. "Monte–Carlo simulations of fusion kinetic processes used to evaluate diagnostic techniques for laser target plasma." Laser and Particle Beams 16, no. 1 (March 1998): 129–51. http://dx.doi.org/10.1017/s0263034600011836.

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Review and systematization of our investigations in thermonuclear (TN) plasma particle diagnostic methods are presented. The proposed diagnostic schemes are based on direct numerical simulations of nuclear reaction products kinetics in a dense hot plasma with the following interpretation of the results by means of analytical scaling relations for charged particles energy loss in plasma with arbitrary degeneration of electron gas. The simulations of the kinetic equations system solution for TN particles is carried out by TERAcode based on Monte–Carlo method. The diagnostic schemes are presented in the form of families of isoline curves at the (ρR, T)-plane which are related to the constant values of measured spectrum characteristics. The searching plasma parameters ρR and temperature T are determined by points of interceptions of curves related to the distinct characteristics. The ranges of applicability of different methods of particle diagnostics are investigated in detail.

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Kilic, Zeliha, Max Schweiger, Camille Moyer, and Steve Pressé. "Monte Carlo samplers for efficient network inference." PLOS Computational Biology 19, no. 7 (July 18, 2023): e1011256. http://dx.doi.org/10.1371/journal.pcbi.1011256.

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Accessing information on an underlying network driving a biological process often involves interrupting the process and collecting snapshot data. When snapshot data are stochastic, the data’s structure necessitates a probabilistic description to infer underlying reaction networks. As an example, we may imagine wanting to learn gene state networks from the type of data collected in single molecule RNA fluorescence in situ hybridization (RNA-FISH). In the networks we consider, nodes represent network states, and edges represent biochemical reaction rates linking states. Simultaneously estimating the number of nodes and constituent parameters from snapshot data remains a challenging task in part on account of data uncertainty and timescale separations between kinetic parameters mediating the network. While parametric Bayesian methods learn parameters given a network structure (with known node numbers) with rigorously propagated measurement uncertainty, learning the number of nodes and parameters with potentially large timescale separations remain open questions. Here, we propose a Bayesian nonparametric framework and describe a hybrid Bayesian Markov Chain Monte Carlo (MCMC) sampler directly addressing these challenges. In particular, in our hybrid method, Hamiltonian Monte Carlo (HMC) leverages local posterior geometries in inference to explore the parameter space; Adaptive Metropolis Hastings (AMH) learns correlations between plausible parameter sets to efficiently propose probable models; and Parallel Tempering takes into account multiple models simultaneously with tempered information content to augment sampling efficiency. We apply our method to synthetic data mimicking single molecule RNA-FISH, a popular snapshot method in probing transcriptional networks to illustrate the identified challenges inherent to learning dynamical models from these snapshots and how our method addresses them.

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Sun, Yi, and Qi Wang. "Modeling and simulations of multicellular aggregate self-assembly in biofabrication using kinetic Monte Carlo methods." Soft Matter 9, no. 7 (2013): 2172. http://dx.doi.org/10.1039/c2sm27090k.

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Filiciotto, F., A. Jinaphanh, and A. Zoia. "SUPER-HISTORY METHODS FOR ADJOINT-WEIGHTED TALLIES IN MONTE CARLO TIME EIGENVALUE CALCULATIONS." EPJ Web of Conferences 247 (2021): 04008. http://dx.doi.org/10.1051/epjconf/202124704008.

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Time eigenvalues emerge in several key applications related to neutron transport problems, including reactor start-up and reactivity measurements. In this context, experimental validation and uncertainty quantification would demand to assess the variation of the dominant time eigenvalue in response to a variation of nuclear data. Recently, we proposed the use of a Generalized Iterated Fission Probability method (G-IFP) to compute adjoint-weighted tallies, such as kinetic parameters, perturbations and sensitivity coefficients, for Monte Carlo time (or alpha) eigenvalue calculations. With the massive use of parallel Monte Carlo calculations, it would be therefore useful to trade the memory burden of the G-IFP method (which is comparable to that of the standard IFP method for k-eigenvalue problems) for computation time and to rely on history-based schemes for such adjoint-weighted tallies. For this purpose, we investigate the use of the super-history method as applied to estimating adjoint-weighted tallies within the α-k power iteration, based on previous work on k-eigenvalue problems. Verification of the algorithms is performed on some simple preliminary tests where analytic solutions exist. In addition, the performances of the proposed method are assessed by comparing the super-history and the G-IFP methods for the same sets of benchmark problems.

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Muralidharan, Pradyumna, Dragica Vasileska, and Stephen M. Goodnick. "A Kinetic Monte Carlo Approach to Study Transport in Amorphous Silicon." Additional Conferences (Device Packaging, HiTEC, HiTEN, and CICMT) 2015, DPC (January 1, 2015): 000743–58. http://dx.doi.org/10.4071/2015dpc-tp33.

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The constant innovation of devices in the semiconductor industry to improve performance to meet very specific demands has led to the study and exploration of a host of new materials. The use of ternary and quaternary materials, organic materials, amorphous silicon are a few examples of promising alternatives to traditional crystalline silicon. However, the use of novel materials presents a unique challenge for a theoretical analysis using device simulation. Traditional device simulation methods are sometimes not enough to capture the nuances of transport. In this paper we study transport through amorphous silicon to evaluate it's role in a-Si/c-Si HIT Cell and it's potential for other device operations by the use of Kinetic Monte Carlo (KMC) method. As an amorphous materials lack long range order, it is near impossible to create a bandstructure for it and conduct Ensemble Monte Carlo (EMC) simulations to study transport through the material. Also, the presence of many defects creates localized states below the conduction band which facilitates hopping transport. As hopping transport happens through a series of capture and emission processes, the time scales of these mechanisms render the use of a traditional EMC unfeasible. It is crucial to understand the nature of the defects/traps that are present in the amorphous material. Understanding defect assisted transport is the key to understanding how the amorphous silicon layer will affect overall device performance. Defect assisted transport (DAT) can be conducted in the amorphous material through various different mechanisms such as capture of carriers by a defect, tunneling emission, Pool-Frenkel emission, defect to defect transition etc. Using the Kinetic Monte Carlo approach we will be able to simulate individual charge carriers that interact with point like defects over a period of time. However, the transient nature of the KMC is not limited to short time scales. Also, the suggested method can be used to study the effect of many transition mechanisms. This method facilitates the simulation of the many individual steps in a Markovian chain that mimics carriers moving through various defects. We study hopping transport through a triangular barrier with use of phonon assisted mechanisms to analyze current and carrier transit times. The distribution function is also analyzed before and after defect assisted transport. One of our primary objectives is to study the role played by the amorphous silicon layer in determining overall device performance of a-Si/c-Si HIT Cell.

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Khromov, Konstantin, Frédéric Soisson, Andrey Stroev, and Valentin Vaks. "Stochastic Statistical Theory of Precipitation in Metastable Alloys with Application to Fe-Cu and Fe-Cu-Mn Alloy Systems." Solid State Phenomena 172-174 (June 2011): 1146–55. http://dx.doi.org/10.4028/www.scientific.net/ssp.172-174.1146.

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The consistent and computationally efficient stochastic statistical approach is suggestedto study kinetics of decomposition of metastable alloys. An important parameter of the theory is thesize of locally equilibrated regions at the nucleation stage which is estimated using the ``maximumthermodynamic gain'' principle suggested. For several realistic models of Fe-Cu alloys studied, the re-sults obtained agree well with kinetic Monte Carlo simulations. Application of methods developed tostudies of decomposition of Fe-Cu-Mn alloys revealed a great sensitivity of evolution to both the con-figurational and kinetic interatomic interactions in an alloy. Using for these interactions the availablefirst-principle and CALPHAD estimates, we can well reproduce the peculiar features of decomposi-tion of Fe-Cu-Mn alloys observed in experiments.

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Bos, C., F. Sommer, and Eric J. Mittemeijer. "Multi-Lattice Kinetic Monte Carlo Simulation of Interface Controlled Solid-State Transformations." Solid State Phenomena 129 (November 2007): 41–49. http://dx.doi.org/10.4028/www.scientific.net/ssp.129.41.

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A kinetic Monte Carlo method has been developed for the simulation of interface controlled solid-state transformations to overcome timescale limitations associated with other atomistic simulation methods. In the simulation method the atoms can take place on sites from (at least) two intertwining crystal lattices. To enable the atoms to also take positions between the ideal lattice sites, a collection of randomly placed sites can be included. These ‘random sites’ have a realistic chance to be occupied at the location of the transformation interface and thus allow for irregularities in the atomic structure of the transformation interface. The atoms move by independent, thermally activated jumps. The activation energy for the atomic jumps can be determined for every jump separately based on the arrangement of the neighbouring atoms. The simulation method has been used to study the interface mobility in the austenite to ferrite transformation in iron for different interface orientations. The results obtained indicate that the excess volume associated with the interface plays a key role for the activation enthalpy for the interface mobility. The rate controlling process is the rearrangement of free space at the interface by series of (unfavourable) jumps by different atoms to create a path from the parent to the product phase.

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Králik, Juraj. "Optimal Protection of Reactor Hall Under Nuclear Fuel Container Drop Using Simulation Methods." Transactions of the VŠB – Technical University of Ostrava, Civil Engineering Series. 14, no. 2 (December 1, 2014): 69–78. http://dx.doi.org/10.2478/tvsb-2014-0020.

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Abstract This paper presents of the optimal design of the damping devices cover of reactor hall under impact of nuclear fuel container drop of type TK C30. The finite element idealization of nuclear power plant structure is used in software ANSYS. The steel pipe damper system is proposed for dissipation of the kinetic energy of the container free fall in comparison with the experimental results. The probabilistic and sensitivity analysis of the damping devices was considered on the base of the simulation methods in program AntHill using the Monte Carlo method.

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Dimarco, Giacomo, Piero Foscari, and Lorenzo Pareschi. "A remark on the finite number of particles effect in Monte Carlo methods for kinetic equations." PAMM 7, no. 1 (December 2007): 1041003–4. http://dx.doi.org/10.1002/pamm.200700307.

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Xu, Xiaojun, Tao Yu, and Shi-Jie Chen. "Understanding the kinetic mechanism of RNA single base pair formation." Proceedings of the National Academy of Sciences 113, no. 1 (December 22, 2015): 116–21. http://dx.doi.org/10.1073/pnas.1517511113.

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RNA functions are intrinsically tied to folding kinetics. The most elementary step in RNA folding is the closing and opening of a base pair. Understanding this elementary rate process is the basis for RNA folding kinetics studies. Previous studies mostly focused on the unfolding of base pairs. Here, based on a hybrid approach, we investigate the folding process at level of single base pairing/stacking. The study, which integrates molecular dynamics simulation, kinetic Monte Carlo simulation, and master equation methods, uncovers two alternative dominant pathways: Starting from the unfolded state, the nucleotide backbone first folds to the native conformation, followed by subsequent adjustment of the base conformation. During the base conformational rearrangement, the backbone either retains the native conformation or switches to nonnative conformations in order to lower the kinetic barrier for base rearrangement. The method enables quantification of kinetic partitioning among the different pathways. Moreover, the simulation reveals several intriguing ion binding/dissociation signatures for the conformational changes. Our approach may be useful for developing a base pair opening/closing rate model.

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Travin, Serghei. "Application of the Monte-Carlo Method to the Description of the Dynamics of the Spread of Salvo Pollution Complicated by Adsorption." Chemistry Journal of Moldova 16, no. 2 (December 2021): 46–51. http://dx.doi.org/10.19261/cjm.2021.920.

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The possibilities of application of the Monte-Carlo method for simulating the consequences of pollutants emissions with specific adsorption on the underlying surface were considered. Effective methods of obtaining kinetic curves for the concentration of a pollutant for a selected square on the field and constructing contamination profiles for a specified time are analysed. The estimation of the necessary parameters of the model for obtaining high-quality kinetic curves was performed and recommendations for their optimization are given. Specific fronts for the spot propagation were obtained and visualised.

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Budetta, Paolo. "Some Remarks on the Use of Deterministic and Probabilistic Approaches in the Evaluation of Rock Slope Stability." Geosciences 10, no. 5 (April 30, 2020): 163. http://dx.doi.org/10.3390/geosciences10050163.

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The rock slope stability assessment can be performed by means of deterministic and probabilistic approaches. As the deterministic analysis needs only representative values (generally, the mean value) for each physical and geo-mechanical parameter involved, it does not take into account the variability and uncertainty of geo-structural and geo-mechanical properties of joints. This analysis can be usually carried out using different methods, such as the Limit Equilibrium method or numerical modeling techniques sometimes implemented in graphical tests to identify different failure mechanisms (kinematic approach). Probabilistic methods (kinetic approach) aimed to calculate the slope failure probability, consider all orientations, physical characters and shear strength of joints and not only those recognized as kinematically possible. Consequently, the failure probability can be overestimated. It is, therefore, considered more realistic to perform both kinematic and kinetic analyses and to calculate a conditional probability given by the product of the kinematic and kinetic probabilities assuming that they are statistically independent variables. These approaches have been tested on two rock slopes in the Campanian region of Southern Italy affected by possible plane and wedge failures, respectively. Kinematic and kinetic probabilities have been evaluated both by means of the Markland’s test and the Monte Carlo simulation. Using the Eurocode 7, also a deterministic limit equilibrium analysis was performed. The obtained results were compared and commented on.

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Shong, Bonggeun, and Stacey F. Bent. "Ab initio Simulation of 1D Pattern Formation of Adsorbates on the Ge(100)-2 × 1 Surface." MRS Proceedings 1551 (2013): 81–86. http://dx.doi.org/10.1557/opl.2013.891.

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ABSTRACTIt is known that methanol and ethylene form distinct one-dimensional patterns along the dimer row on the Ge(100)-2 × 1 surface. A unified explanation for the pattern formation is attempted in this study through theoretical methods. Kinetic parameters of the precursor-mediated adsorption of the two molecules are calculated using density functional theory methods. The potential energy surface along the reaction channel was found to vary according to nearest-neighbor occupation. Monte Carlo simulations were performed with calculated kinetic coefficients and assumptions of a one-dimensional lattice with nearest neighbor interactions. The simulation results effectively reproduce the coverage-dependent evolution of longer-range adsorption patterns.

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Görlitz, Linus, Zhenglei Gao, and Walter Schmitt. "Statistical Analysis of Chemical Transformation Kinetics Using Markov-Chain Monte Carlo Methods." Environmental Science & Technology 45, no. 10 (May 15, 2011): 4429–37. http://dx.doi.org/10.1021/es104218h.

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Kurganskaya, Inna, Nikolay Trofimov, and Andreas Luttge. "A Kinetic Monte Carlo Approach to Model Barite Dissolution: The Role of Reactive Site Geometry." Minerals 12, no. 5 (May 18, 2022): 639. http://dx.doi.org/10.3390/min12050639.

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Barite (Ba[SO4]) is one of the promising candidates for sequestration of radioactive waste. Barite can incorporate radium (Ra) and form ideal solid solutions, i.e., (Ba,Ra)[SO4]. Together with isostructural celestite (Sr[SO4]), ternary solid solutions, (Ba,Sr,Ra)[SO4], may exist in natural conditions. Our fundamental understanding of the dissolution kinetics of isostructural sulfates is critically important for a better risk assessment of nuclear waste repositories utilizing this mineral for sequestration. So far, the barite-water interface has been studied with experimental methods and atomistic computer simulations. The direct connection between the molecular scale details of the interface structure and experimental observations at the microscopic scale is not yet well understood. Here, we began to investigate this connection by using a kinetic Monte Carlo approach to simulate the barite dissolution process. We constructed a microkinetic model for the dissolution process and identified the reactive sites. Identification of these sites is important for an improved understanding of the dissolution, adsorption, and crystal growth mechanisms at the barite–water interface. We parameterized the molecular detachment rates by using the experimentally observed etch pit morphologies and atomic step velocities. Our parameterization attempts demonstrated that local lattice coordination is not sufficient to differentiate between the kinetically important sites and estimate their detachment rates. We suggest that the water structure and dynamics at identified sites should substantially influence the detachment rates. However, it will require more work to improve the parameterization of the model by means of Molecular Dynamics and ab initio calculations.

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Bernetti, Mattia, Matteo Masetti, Walter Rocchia, and Andrea Cavalli. "Kinetics of Drug Binding and Residence Time." Annual Review of Physical Chemistry 70, no. 1 (June 14, 2019): 143–71. http://dx.doi.org/10.1146/annurev-physchem-042018-052340.

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The kinetics of drug binding and unbinding is assuming an increasingly crucial role in the long, costly process of bringing a new medicine to patients. For example, the time a drug spends in contact with its biological target is known as residence time (the inverse of the kinetic constant of the drug-target unbinding, 1/ koff). Recent reports suggest that residence time could predict drug efficacy in vivo, perhaps even more effectively than conventional thermodynamic parameters (free energy, enthalpy, entropy). There are many experimental and computational methods for predicting drug-target residence time at an early stage of drug discovery programs. Here, we review and discuss the methodological approaches to estimating drug binding kinetics and residence time. We first introduce the theoretical background of drug binding kinetics from a physicochemical standpoint. We then analyze the recent literature in the field, starting from the experimental methodologies and applications thereof and moving to theoretical and computational approaches to the kinetics of drug binding and unbinding. We acknowledge the central role of molecular dynamics and related methods, which comprise a great number of the computational methods and applications reviewed here. However, we also consider kinetic Monte Carlo. We conclude with the outlook that drug (un)binding kinetics may soon become a go/no go step in the discovery and development of new medicines.

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Chen, H. F., P. Zheng, X. J. Sun, J. Xiao, T. H. Zhu, J. J. Sun, J. Y. Zhu, Z. H. Li, Y. Cao, and J. Yan. "Characterize the grid inefficiency correction of Frisch grid ionization chamber with Garfield and SRIM." Journal of Instrumentation 17, no. 03 (March 1, 2022): P03023. http://dx.doi.org/10.1088/1748-0221/17/03/p03023.

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Abstract Grid inefficiency (GI) correction is critically important to accurately determine the energy and emission angle of charged particles in Frisch grid ionization chambers (FGIC). Presently, three correction methods have been proposed in the literature. All these three methods rely strongly on the accurate and stable calibration between two different electrical chain gains in experiments. To avoid these challenges, they are firstly investigated with Monte Carlo method by using Garfield and SRIM toolkit. Two types of grid wire (diameter 90 and 180 μm) were simulated to investigate the impacts of GI correction on energy correction, and ten different emission angles were adopted to estimate the effects of these correction methods on the angle reconstruction. The simulation results clearly distinguished the differences among these correction methods in energy determination. The correlation analysis (R = 0.99) among these different correction methods indicated that the impact of either correction approach is small on the determination of emission angle. The present work firstly investigates the effects of GI correction on emission angle with Monte Carlo methods, which is the basis of future work to measure the total kinetic energy and mass distribution of Fission Fragment (FF).

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Shakhmatov, Evgeniy, Vladimir Ilyukhin, and Dmitry Mezentsev. "STUDY OF THE STATIC CHARACTERISTICS OF THE WORKSPACE AR600E ROBOT." Journal of Dynamics and Vibroacoustics 5, no. 4 (March 12, 2020): 6–12. http://dx.doi.org/10.18287/2409-4579-2019-5-4-6-12.

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The workspace is one of the most important parameters for evaluating robot flexibility and is important for optimizing robotic configuration, motion planning and control. Firstly, a kinematic model of the manipulator based on its basic structure was put forward. The systems of connection coordinates are established and the direct kinematic solution derived using DH methods. On its basis, the working space of the manipulator analyzed by the Monte Carlo method, based on random probability and software simulation MATLAB for the structural parameters of the robot. A cloud of workspace points has been compiled. Considering the problem of insufficient accuracy of the traditional Monte Carlo method in calculating the working space of the robot, an improved Monte Carlo method using the Beta distribution proposed.

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Hernández-Rivera, Efraín, Veena Tikare, and Lu Min Wang. "Simulation of Radiation-Induced Segregation by the Hybrid Potts-Phase Field Model." Materials Science Forum 783-786 (May 2014): 1872–79. http://dx.doi.org/10.4028/www.scientific.net/msf.783-786.1872.

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A hybrid model of microstructural evolution of a coupled multi–field system that is subjected to ion irradiation is presented. Materials exposed to low energy ion irradiation experience a wide range of radiation effects, e.g. surface roughening and chemical segregation. The hybrid model combines Monte Carlo methods and a phase field model to simulate the kinetic and radiation-induced processes that lead to radiation induced chemical segregation with associated phase transformations of a binary system by differential diffusivity.

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Rohlfs, Ricarda, Cornelius Fischer, Inna Kurganskaya, and Andreas Luttge. "Crystal Dissolution Kinetics Studied by a Combination of Monte Carlo and Voronoi Methods." Minerals 8, no. 4 (March 24, 2018): 133. http://dx.doi.org/10.3390/min8040133.

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Ramasubramaniam, Ashwin, and Emily A. Carter. "Coupled Quantum–Atomistic and Quantum–Continuum Mechanics Methods in Materials Research." MRS Bulletin 32, no. 11 (November 2007): 913–18. http://dx.doi.org/10.1557/mrs2007.188.

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AbstractThe interface of quantum mechanics methods with classical atomistic simulation techniques, such as molecular dynamics and Monte Carlo, continues to be an area of considerable promise and interest. Such coupled quantum–atomistic approaches have been developed and employed, for example, to gain a comprehensive understanding of the energetics, kinetics, and dynamics of chemical processes involving surfaces and interfaces of hard materials. More recently, it has become possible to directly couple first-principles electronic structure techniques to continuum solid mechanics, either on the fly with feedback between length scales or by information passing between length scales. We discuss, with tutorial examples, the merging of quantum mechanics with molecular dynamics and Monte Carlo simulations, as well as quantum–continuum coupled techniques. We illustrate the opportunities offered by incorporation of information from quantum mechanics (reducing assumptions in higher length-scale models) and outline the challenges associated with achieving full predictive capability for the behavior of materials.

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Wasaye, Muhammad, Hui Wang, Huaqing Zheng, Pengcheng Long, and Hamza Naeem. "The hybrid model for sampling multiple elastic scattering angular deflections based on Goudsmit-Saunderson theory." Nuclear Technology and Radiation Protection 32, no. 3 (2017): 229–35. http://dx.doi.org/10.2298/ntrp1703229w.

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An algorithm for the Monte Carlo simulation of electron multiple elastic scattering based on the framework of SuperMC (Super Monte Carlo simulation program for nuclear and radiation process) is presented. This paper describes efficient and accurate methods by which the multiple scattering angular deflections are sampled. The Goudsmit-Saunderson theory of multiple scattering has been used for sampling angular deflections. Differential cross-sections of electrons and positrons by neutral atoms have been calculated by using Dirac partial wave program ELSEPA. The Legendre coefficients are accurately computed by using the Gauss-Legendre integration method. Finally, a novel hybrid method for sampling angular distribution has been developed. The model uses efficient rejection sampling method for low energy electrons (<500 keV) and larger path lengths (>500 mean free paths). For small path lengths, a simple, efficient and accurate analytical distribution function has been proposed. The later uses adjustable parameters determined from the fitting of Goudsmith-Saunderson angular distribution. A discussion of the sampling efficiency and accuracy of this newly developed algorithm is given. The efficiency of rejection sampling algorithm is at least 50 % for electron kinetic energies less than 500 keV and longer path lengths (>500 mean free paths). Monte Carlo Simulation results are then compared with measured angular distributions of Ross et al. The comparison shows that our results are in good agreement with experimental measurements.

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Journal articles on the topic 'Kinetic Monte Carlo Methods'

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