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Molecular exchange Monte Carlo: A generalized method for identity exchanges in grand canonical Monte Carlo simulations.

Mohammad Soroush BarhaghiKorosh TorabiYounes NejahiLoren SchwiebertJeffrey J Potoff
Published in: The Journal of chemical physics (2018)
A generalized identity exchange algorithm is presented for Monte Carlo simulations in the grand canonical ensemble. The algorithm, referred to as molecular exchange Monte Carlo, may be applied to multicomponent systems of arbitrary molecular topology and provides significant enhancements in the sampling of phase space over a wide range of compositions and temperatures. Three different approaches are presented for the insertion of large molecules, and the pros and cons of each method are discussed. The performance of the algorithms is highlighted through grand canonical Monte Carlo histogram-reweighting simulations performed on a number of systems, which include methane+n-alkanes, butane+perfluorobutane, water+impurity, and 2,2,4-trimethylpentane+neopentane. Relative acceptance efficiencies for molecule transfers of up to 400 times that of standard configurational-bias Monte Carlo are obtained.
Keyphrases
  • monte carlo
  • machine learning
  • deep learning
  • magnetic resonance
  • convolutional neural network