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.

Feedback-optimized parallel tempering Monte Carlo

Abstract: We introduce an algorithm for systematically improving the efficiency of parallel tempering Monte Carlo simulations by optimizing the simulated temperature set. Our approach is closely related to a recently introduced adaptive algorithm that optimizes the simulated statistical ensemble in generalized broad-histogram Monte Carlo simulations. Conventionally, a temperature set is chosen in such a way that the acceptance rates for replica swaps between adjacent temperatures are independent of the temperature and large enough to ensure frequent swaps. In this paper, we show that by choosing the temperatures with a modified version of the optimized ensemble feedback method we can minimize the round-trip times between the lowest and highest temperatures which effectively increases the efficiency of the parallel tempering algorithm. In particular, the density of temperatures in the optimized temperature set increases at the 'bottlenecks' of the simulation, such as phase transitions. In turn, the acceptance rates are now temperature dependent in the optimized temperature ensemble. We illustrate the feedback-optimized parallel tempering algorithm by studying the two-dimensional Ising ferromagnet and the two-dimensional fully frustrated Ising model, and briefly discuss possible feedback schemes for systems that require configurational averages, such as spin glasses.

Particle-transport simulation with the Monte Carlo method

Abstract: Attention is focused on the application of the Monte Carlo method to particle transport problems, with emphasis on neutron and photon transport. Topics covered include sampling methods, mathematical prescriptions for simulating particle transport, mechanics of simulating particle transport, neutron transport, and photon transport. A literature survey of 204 references is included. (GMT)

Sequential Monte Carlo Methods for Statistical Analysis of Tables

Abstract: We describe a sequential importance sampling (SIS) procedure for analyzing two-way zero–one or contingency tables with fixed marginal sums. An essential feature of the new method is that it samples the columns of the table progressively according to certain special distributions. Our method produces Monte Carlo samples that are remarkably close to the uniform distribution, enabling one to approximate closely the null distributions of various test statistics about these tables. Our method compares favorably with other existing Monte Carlo-based algorithms, and sometimes is a few orders of magnitude more efficient. In particular, compared with Markov chain Monte Carlo (MCMC)-based approaches, our importance sampling method not only is more efficient in terms of absolute running time and frees one from pondering over the mixing issue, but also provides an easy and accurate estimate of the total number of tables with fixed marginal sums, which is far more difficult for an MCMC method to achieve.

Structural modelling of glasses using reverse Monte Carlo simulation

Abstract: ONE of the main difficulties in the study of glasses and other disordered materials is the production of structural models that agree quantitatively with diffraction data. In normal Monte Carlo simulation, an initial structure is allowed to rearrange in such a way that its energy is minimized. Reverse Monte Carlo simulation1 is a newly developed technique in which a structural model is adjusted so as to minimize instead the difference between the calculated diffraction pattern and that measured experimentally, so that good agreement is inevitable. No interatomic potential is required. Here we illustrate the potential of this method by fitting the structure of vitreous silica simultaneously to X-ray and neutron diffraction data. The result, a (mostly) continuous random network of corner-sharing SiO4 tetrahedra, is consistent with other models but, unlike them, is derived solely from the data.

Transfer-matrix method and Monte Carlo simulation in quantum spin systems

Abstract: Transfer-matrix methods for quantum spin systems are formulated and their limiting properties are studied rigorously. The present formulation is applied explicitly to an exactly soluble transverse Ising model. A computer implementation of the two-dimensional triangular antiferromagnetic quantum Heisenberg model is also proposed to study Anderson's picture of the dynamic coherence of the phase of singlet pairs.