Improving the sampling efficiency of Monte Carlo molecular simulations: an evolutionary approach

Abstract

We present a new approach in order to improve the convergence of Monte Carlo (MC) simulations of molecular systems belonging to complex energetic landscapes: the problem is redefined in terms of the dynamic allocation of MC move frequencies depending on their past efficiency, measured with respect to a relevant sampling criterion. We introduce various empirical criteria with the aim of accounting for the proper convergence in phase space sampling. The dynamic allocation is performed over parallel simulations by means of a new evolutionary algorithm involving 'immortal' individuals. The method is bench marked with respect to conventional procedures on a model for melt linear polyethylene. We record significant improvement in sampling efficiencies, thus in computational load, while the optimal sets of move frequencies are liable to allow interesting physical insights into the particular systems simulated. This last aspect should provide a new tool for designing more efficient new MC moves.

Accelerating molecular simulations by reversible mapping between local minima

“…29 It is worth noting that while the focus of the present work has been on performing Markov chain Monte Carlo simulations of atomistically detailed condensed matter, the MinMap framework is extremely general. These include the move types and their mixing proportions, the number of active atoms, the number of excitations, the number of mutation stages, and the minimization procedure and its corresponding potential function.…”

A primer on using Monte Carlo simulations to evaluate marksmanship

“…Any Monte Carlo simulation is defined first and foremost by variables and associated data supporting the model and its simulation. Economic simulations use financial variables (Arnold and Yildiz, 2015;Mun, 2006), whereas biological simulations may use energy transfer or molecule movement (Berney and Danuser, 2003;Leblanc et al, 2003). The first consideration is therefore the data that supports model variables-and the assumptions that go with the data.…”