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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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Journal ArticleDOI
TL;DR: In this paper, a class of Monte Carlo algorithms incorporating absorbing Markov chains is presented to study the escape from the metastable state in the two-dimensional square-lattice nearest-neighbor Ising ferromagnet in an unfavorable applied field.
Abstract: A class of Monte Carlo algorithms which incorporate absorbing Markov chains is presented. In a particular limit, the lowest order of these algorithms reduces to the $n$-fold way algorithm. These algorithms are applied to study the escape from the metastable state in the two-dimensional square-lattice nearest-neighbor Ising ferromagnet in an unfavorable applied field, and the agreement with theoretical predictions is very good. It is demonstrated that the higher-order algorithms can be many orders of magnitude faster than either the traditional Monte Carlo or $n$-fold way algorithms.

117 citations

Journal ArticleDOI
TL;DR: In this article, an iterative strategy is used to determine a response surface that is able to fit the limit state function in the neighbourhood of the design point, where the locations of the sample points used to evaluate the free parameters of the response surface are chosen according to the importance sensitivity of each random variable.

117 citations

Journal ArticleDOI
TL;DR: It is found that the new approach greatly improves the structural description, alleviating the common problem in standard reverse Monte Carlo method (RMC) of generating structures with a high proportion of unphysical small rings.
Abstract: An improved method for the modelling of carbon structures based on a hybrid reverse Monte Carlo (HRMC) method is presented. This algorithm incorporates an accurate environment dependent interaction potential (EDIP) in conjunction with the commonly used constraints derived from experimental data. In this work, we compare this new method with other modelling results for a small system of 2.9 g/cc amorphous carbon. We find that the new approach greatly improves the structural description, alleviating the common problem in standard reverse Monte Carlo method (RMC) of generating structures with a high proportion of unphysical small rings. The advantage of our method is that larger systems can now be modelled, allowing the incorporation of mesoscopic scale features.

117 citations

Journal ArticleDOI
TL;DR: Stochastic roadmap simulation (SRS) is introduced as a new computational approach for exploring the kinetics of molecular motion by simultaneously examining multiple pathways and converges to the same distribution as Monte Carlo simulation.
Abstract: Classic molecular motion simulation techniques, such as Monte Carlo (MC) simulation, generate motion pathways one at a time and spend most of their time in the local minima of the energy landscape defined over a molecular conformation space. Their high computational cost prevents them from being used to compute ensemble properties (properties requiring the analysis of many pathways). This paper introduces stochastic roadmap simulation (SRS) as a new computational approach for exploring the kinetics of molecular motion by simultaneously examining multiple pathways. These pathways are compactly encoded in a graph, which is constructed by sampling a molecular conformation space at random. This computation, which does not trace any particular pathway explicitly, circumvents the local-minima problem. Each edge in the graph represents a potential transition of the molecule and is associated with a probability indicating the likelihood of this transition. By viewing the graph as a Markov chain, ensemble properties can be efficiently computed over the entire molecular energy landscape. Furthermore, SRS converges to the same distribution as MC simulation. SRS is applied to two biological problems: computing the probability of folding, an important order parameter that measures the "kinetic distance" of a protein's conformation from its native state; and estimating the expected time to escape from a ligand-protein binding site. Comparison with MC simulations on protein folding shows that SRS produces arguably more accurate results, while reducing computation time by several orders of magnitude. Computational studies on ligand-protein binding also demonstrate SRS as a promising approach to study ligand-protein interactions.

117 citations

Journal ArticleDOI
TL;DR: This paper deals with computing based on various forms of random sampling, or Monte Carlo methods, with applications to evaluate integrals and simple simulation.
Abstract: This paper deals with computing based on various forms of random sampling, or Monte Carlo methods. The author illustrates with applications to evaluate integrals and simple simulation, before explaining the prescription that inspired the paper.

116 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202313
202242
20212
20203
20198
201853