Topic
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.
Papers published on a yearly basis
Papers
More filters
TL;DR: In this paper, a variant of the sequential Monte Carlo sampler by incorporating the partial rejection control mechanism of Liu (2001) is presented, which can reduce the variance of the incremental importance weights when compared with standard sequential Monte-Carlo samplers.
Abstract: We present a variant of the sequential Monte Carlo sampler by incorporating the partial rejection control mechanism of Liu (2001). We show that the resulting algorithm can be considered as a sequential Monte Carlo sampler with a modified mutation kernel. We prove that the new sampler can reduce the variance of the incremental importance weights when compared with standard sequential Monte Carlo samplers, and provide a central limit theorem. Finally, the sampler is adapted for application under the challenging approximate Bayesian computation modelling framework.
77 citations
TL;DR: An energy basin finding algorithm for identifying the states in absorbing Markov chains used for accelerating kinetic Monte Carlo simulations out of trapping energy basins that can be several orders of magnitude faster than standard KMC simulations is presented.
Abstract: We present an energy basin finding algorithm for identifying the states in absorbing Markov chains used for accelerating kinetic Monte Carlo (KMC) simulations out of trapping energy basins. The algorithm saves groups of states corresponding to basic energy basins in which there is (i) a minimum energy saddle point and (ii) in moving away from the minimum the saddle point energies do not decrease between successive moves. When necessary, these groups are merged to help the system escape basins of basins. Energy basins are identified either as the system visits states, or by exploring surrounding states before the system visits them. We review exact and approximate methods for accelerating KMC simulations out of trapping energy basins and implement them within our algorithm. Its flexibility to store varying numbers of states, and ability to merge sets of saved states as the program runs, allows it to efficiently escape complicated trapping energy basins. Through simulations of vacancy-As cluster dissolution in Si, we demonstrate our algorithm can be several orders of magnitude faster than standard KMC simulations.
77 citations
TL;DR: It is argued that autocorrelation time is the most important test for convergence, as it directly connects to the uncertainty on the sampling estimate of any quantity of interest.
Abstract: Markov Chain Monte Carlo (MCMC) methods for sampling probability density functions (combined with abundant computational resources) have transformed the sciences, especially in performing probabilistic inferences, or fitting models to data. In this primarily pedagogical contribution, we give a brief overview of the most basic MCMC method and some practical advice for the use of MCMC in real inference problems. We give advice on method choice, tuning for performance, methods for initialization, tests of convergence, troubleshooting, and use of the chain output to produce or report parameter estimates with associated uncertainties. We argue that autocorrelation time is the most important test for convergence, as it directly connects to the uncertainty on the sampling estimate of any quantity of interest. We emphasize that sampling is a method for doing integrals; this guides our thinking about how MCMC output is best used.
77 citations
TL;DR: In this article, a determinantal grand-canonical method is proposed based on a stochastic series expansion for the partition function in the interaction representation for finite fermionic systems with non-local interactions.
Abstract: Numerically exact continuous-time Quantum Monte Carlo algorithm for finite fermionic systems with non-local interactions is proposed. The scheme is particularly applicable for general multi-band time-dependent correlations since it does not invoke Hubbard-Stratonovich transformation. The present determinantal grand-canonical method is based on a stochastic series expansion for the partition function in the interaction representation. The results for the Green function and for the time-dependent susceptibility of multi-orbital super-symmetric impurity model with a spin-flip interaction are presented.
77 citations
TL;DR: In this paper, a stable quantum Monte Carlo approach for computing forces between atoms in a molecule is presented, using the standard Hellmann-Feynman expression (local force expressed as the derivative of the total potential energy with respect to the internuclear coordinates).
Abstract: We present a simple and stable quantum Monte Carlo approach for computing forces between atoms in a molecule. In this approach we propose to use as Monte Carlo estimator of the force the standard Hellmann–Feynman expression (local force expressed as the derivative of the total potential energy with respect to the internuclear coordinates). Invoking a recently introduced zero-variance principle it is shown how the infinite variance associated with the Hellmann–Feynman estimator can be made finite by introducing some suitably renormalized expression for the force. Practical calculations for the molecules H2, Li2, LiH, and C2 illustrate the efficiency of the method.
77 citations