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Monte Carlo molecular modeling
About: Monte Carlo molecular modeling is a research topic. Over the lifetime, 11307 publications have been published within this topic receiving 409122 citations.
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TL;DR: In this paper, the formation free energy of clusters in a supersaturated vapor is obtained by a constrained Monte Carlo technique, where the key feature is to set an upper limit to the size of cluster, which serves essentially as an extra thermodynamic variable that constrains the system.
Abstract: The formation free energy of clusters in a supersaturated vapor is obtained by a constrained Monte Carlo technique. A key feature of this approach is to set an upper limit to the size of cluster. This maximum cluster size serves essentially as an extra thermodynamic variable that constrains the system. As a result, clusters larger than the critical cluster of nucleation in the supersaturated vapor can no longer grow beyond the limiting size. Like changing the overall density of the system, changing the maximum cluster size also results in a different supersaturation and thereby a different formation free energy. However, at the same supersaturation and temperature it is found that the formation free energy has a unique value, independent of the upper limit of cluster size. The predicted size of critical cluster of nucleation is found to be consistent with the nucleation theorem as well as previous results using different simulation approaches.
73 citations
TL;DR: In this article, the Monte Carlo simulations based on the heat bath algorithm are implemented for the following classical spin systems: (i) the continuous-spin Ising model; (ii) the XY model and (iii) the Heisenberg model.
Abstract: The Monte Carlo simulations based on the 'heat bath' algorithms are implemented for the following classical spin systems: (i) the continuous-spin Ising model; (ii) the XY model and (iii) the Heisenberg model.
73 citations
TL;DR: In this article, Monte Carlo techniques are used for a complete study of unipolar semiconducting components for both diffusion noise properties and static characteristics, taking into account space-charge reaction by integration of Poisson's equation.
Abstract: It is shown in this paper that Monte Carlo techniques are available for a complete study of unipolar semiconducting components for both diffusion noise properties and static characteristics. This necessitates taking into account space-charge reaction by integration of Poisson's equation. Two possible methods are proposed and carried out in a unidimensional treatment. Emphasis is given to the various problems which are encountered, especially those concerning the reliability of the solutions. In the first of the two methods (single carrier) the validity of classical electrokinetics equations is investigated, showing the influence of possible relaxation effects in a component. In the second method (multicarrier), it is shown that diffusion noise properties are approachable by observation and spectral analysis of current or voltage fluctuations at the ends of the component.
72 citations
TL;DR: A general purpose Monte Carlo computer code for the calculation of radiative exchange factors in three-dimensional enclosures with a nonparticipating medium is described in this article, where the effect of input file parameters on computer lime usage is investigated, and rates of convergence are calculated.
Abstract: A general purpose Monte Carlo computer code for the calculation of radiative exchange factors in three-dimensional enclosures with a nonparticipating medium is described. Capabilities of the code include mixed specular and diffuse reflection models, banded spectral material properties, transmission through external surfaces, and simulation of beam radiation. The ray tracing and surface interaction routines are described, as well as the shading algorithms. The effect of input file parameters on computer lime usage is investigated, and rates of convergence are calculated. Results for run time and accuracy are in good agreement with predictions.
72 citations
TL;DR: This work illustrates the capabilities of population-annealing Monte Carlo by computing ground states of the three-dimensional Ising spin glass with Gaussian disorder, while comparing to simulated-annealed and parallel-tempering Monte Carlo.
Abstract: Population annealing is a Monte Carlo algorithm that marries features from simulated-annealing and parallel-tempering Monte Carlo. As such, it is ideal to overcome large energy barriers in the free-energy landscape while minimizing a Hamiltonian. Thus, population-annealing Monte Carlo can be used as a heuristic to solve combinatorial optimization problems. We illustrate the capabilities of population-annealing Monte Carlo by computing ground states of the three-dimensional Ising spin glass with Gaussian disorder, while comparing to simulated-annealing and parallel-tempering Monte Carlo. Our results suggest that population annealing Monte Carlo is significantly more efficient than simulated annealing but comparable to parallel-tempering Monte Carlo for finding spin-glass ground states.
72 citations