In complex systems such as spin systems and protein systems, conventional simulations in the canonical ensemble will get trapped in states of energy local minima. We employ the generalized-ensemble algorithms in order...
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In complex systems such as spin systems and protein systems, conventional simulations in the canonical ensemble will get trapped in states of energy local minima. We employ the generalized-ensemble algorithms in order to overcome this multiple-minima problem. Three well-known generalized-ensemble algorithms, namely, multicanonical algorithm, simulated tempering, and replica-exchange method, are described. We then present three new generalized-ensemble algorithms based on the combinations of the three methods. Effectiveness of the new methods are tested with a Potts model and protein systems. (C) 2002 Elsevier Science B.V. All rights reserved.
Monte Carlo simulations based on simulated annealing and multicanonical algorithm have been performed to predict the secondary and tertiary structures of oligopeptide systems. Two oligopeptides, C-peptide of ribonucle...
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Monte Carlo simulations based on simulated annealing and multicanonical algorithm have been performed to predict the secondary and tertiary structures of oligopeptide systems. Two oligopeptides, C-peptide of ribonuclease A and the fragment BPTI(16-36) of bovine pancreatic trypsin inhibitor, were studied. Only the amino-acid sequence information was used as input and initial conformations were randomly generated. The lowest-energy conformations obtained have alpha-helix structure and beta-sheet structure for C-peptide and BPTI(16-36), respectively, in remarkable agreement with experimental results.
Inference for a complex system with a rough energy landscape is a central topic in Monte Carlo computation. Motivated by the successes of the Wang-Landau algorithm in discrete systems, we generalize the algorithm to c...
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Inference for a complex system with a rough energy landscape is a central topic in Monte Carlo computation. Motivated by the successes of the Wang-Landau algorithm in discrete systems, we generalize the algorithm to continuous systems. The generalized algorithm has some features that conventional Monte Carlo algorithms do not have. First, it provides a new method for Monte Carlo integration based on stochastic approximation;second, it is an excellent tool for Monte Carlo optimization. In an appropriate setting, the algorithm can lead to a random walk in the energy space, and thus it can sample relevant parts of the sample space, even in the presence of many local energy minima. The generalized algorithm can be conveniently used in many problems of Monte Carlo integration and optimization, for example, normalizing constant estimation, model selection, highest posterior density interval construction, and function optimization. Our numerical results show that the algorithm outperforms simulated annealing and parallel tempering in optimization for the system with a rough energy landscape. Some theoretical results on the convergence of the algorithm are provided.
The growing adoption of generalized-ensemble algorithms for biomolecular simulation has resulted in a resurgence in the use of the weighted histogram analysis method (WHAM) to make use of all data generated by these s...
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The growing adoption of generalized-ensemble algorithms for biomolecular simulation has resulted in a resurgence in the use of the weighted histogram analysis method (WHAM) to make use of all data generated by these simulations. Unfortunately, the original presentation of WHAM by Kumar et al. is not directly applicable to data generated by these methods. WHAM was originally formulated to combine data from independent samplings of the canonical ensemble, whereas many generalized-ensemble algorithms sample from mixtures of canonical ensembles at different temperatures. Sorting configurations generated from a parallel tempering simulation by temperature obscures the temporal correlation in the data and results in an improper treatment of the statistical uncertainties used in constructing the estimate of the density of states. Here we present variants of WHAM, STWHAM and PTWHAM, derived with the same set of assumptions, that can be directly applied to several generalized ensemble algorithms, including simulated tempering, parallel tempering (better known as replica-exchange among temperatures), and replica-exchange simulated tempering. We present methods that explicitly capture the considerable temporal correlation in sequentially generated configurations using autocorrelation analysis. This allows estimation of the statistical uncertainty in WHAM estimates of expectations for the canonical ensemble. We test the method with a one-dimensional model system and then apply it to the estimation of potentials of mean force from parallel tempering simulations of the alanine dipeptide in both implicit and explicit solvent.
We investigate the dynamical behavior of the recently proposed multibondic cluster Monte Carlo algorithm in applications to the three-dimensional q-state Potts models with q = 3, 4, and 5 in the vicinity of their firs...
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We investigate the dynamical behavior of the recently proposed multibondic cluster Monte Carlo algorithm in applications to the three-dimensional q-state Potts models with q = 3, 4, and 5 in the vicinity of their first-order phase transition points. For comparison we also report simulations with the standard multicanonical algorithm. Similar to the findings in two dimensions, we how that for the multibondic cluster algorithm the dependence of the autocorrelation time tau on the system size Vis well described by the power law tau proportional to V-alpha, and that the dynamical exponent a is consistent with the optimal random walk estimate alpha = 1. For the multicanonical simulations we obtain, as expected, a larger value of alpha approximate to 1.2.
In complex systems such as spin glasses and proteins, conventional simulations in the canonical ensemble will get trapped in states of energy local minima. We employ the simulated annealing method and generalized-ense...
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In complex systems such as spin glasses and proteins, conventional simulations in the canonical ensemble will get trapped in states of energy local minima. We employ the simulated annealing method and generalized-ensemble algorithms in order to overcome this multiple-minima problem. Besides simulated annealing, three well-known generalized-ensemble algorithms, namely, multicanonical algorithm, simulated tempering, and replica-exchange method, are described. We then present three new generalized-ensemble algorithms based on the combinations of the three methods. (C) 2001 Elsevier Science B.V. All rights reserved.
作者:
Okamoto, YukoNagoya Univ
Grad Sch Sci Dept Phys Nagoya Aichi 4648602 Japan Nagoya Univ
Grad Sch Sci Struct Biol Res Ctr Nagoya Aichi 4648602 Japan Nagoya Univ
Grad Sch Engn Ctr Computat Sci Nagoya Aichi 4648603 Japan
In this article, we review the generalised-ensemble algorithms. Three well-known methods, namely multicanonical algorithm, simulated tempering and replica-exchange method, are described first. Both Monte Carlo and mol...
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In this article, we review the generalised-ensemble algorithms. Three well-known methods, namely multicanonical algorithm, simulated tempering and replica-exchange method, are described first. Both Monte Carlo and molecular dynamics versions of the algorithms are presented. We then present further extensions of the above three methods. Finally, we discuss the relations among multicanonical algorithm, Wang-Landau method and metadynamics.
In complex systems with many degrees of freedom such as biomolecular systems, conventional Monte Carlo and molecular dynamics simulations in canonical ensemble or isobaric-isothermal ensemble suffer from the multiple-...
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In complex systems with many degrees of freedom such as biomolecular systems, conventional Monte Carlo and molecular dynamics simulations in canonical ensemble or isobaric-isothermal ensemble suffer from the multiple-minima problem, resulting in entrapment in states of energy local minima. A simulation in generalized ensemble performs a random walk in specified variables and overcomes this difficulty. In this article we review the generalized-ensemble algorithms. multicanonical algorithm is described first. In this method, a random walk in potential energy space is realized and the simulation can avoid the multiple-minima problem. We then present two new generalized-ensemble algorithms, namely multioverlap algorithm and multibaric-multithermal algorithm, which are multi-variable/multi-dimensional extensions of the multicanonical algorithm. In the former method, a random walk in overlap space is realized, and in the latter that in both potential energy space and volume space is obtained. Emphasis is laid in the description of the molecular dynamics versions of these algorithms.
In complex systems such as spin glasses and proteins, conventional simulations in the canonical ensemble will get trapped in states of energy local minima. We employ the simulated annealing method and generalized-ense...
详细信息
In complex systems such as spin glasses and proteins, conventional simulations in the canonical ensemble will get trapped in states of energy local minima. We employ the simulated annealing method and generalized-ensemble algorithms in order to overcome this multiple-minima problem. Besides simulated annealing, three well-known generalized-ensemble algorithms, namely, multicanonical algorithm, simulated tempering, and replica-exchange method, are described. We then present three new generalized-ensemble algorithms based on the combinations of the three methods. (C) 2001 Elsevier Science B.V. All rights reserved.
In complex systems such as spin systems and protein systems, conventional simulations in the canonical ensemble will get trapped in states of energy local minima. We employ the generalized-ensemble algorithms in order...
详细信息
In complex systems such as spin systems and protein systems, conventional simulations in the canonical ensemble will get trapped in states of energy local minima. We employ the generalized-ensemble algorithms in order to overcome this multiple-minima problem. Three well-known generalized-ensemble algorithms, namely, multicanonical algorithm, simulated tempering, and replica-exchange method, are described. We then present three new generalized-ensemble algorithms based on the combinations of the three methods. Effectiveness of the new methods are tested with a Potts model and protein systems. (C) 2002 Elsevier Science B.V. All rights reserved.
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