Orkoulas G
Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, California 90095, USA.
J Chem Phys. 2007 Aug 28;127(8):084106. doi: 10.1063/1.2759923.
A new grand canonical Monte Carlo algorithm for continuum fluid models is proposed. The method is based on a generalization of sequential Monte Carlo algorithms for lattice gas systems. The elementary moves, particle insertions and removals, are constructed by analogy with those of a lattice gas. The updating is implemented by selecting points in space (spatial updating) either at random or in a definitive order (sequential). The type of move, insertion or removal, is deduced based on the local environment of the selected points. Results on two-dimensional square-well fluids indicate that the sequential version of the proposed algorithm converges faster than standard grand canonical algorithms for continuum fluids. Due to the nature of the updating, additional reduction of simulation time may be achieved by parallel implementation through domain decomposition.
提出了一种用于连续流体模型的新的巨正则蒙特卡罗算法。该方法基于对晶格气体系统的序贯蒙特卡罗算法的推广。基本移动,即粒子的插入和移除,是通过与晶格气体的基本移动类比构建的。更新通过随机或按确定顺序(序贯)在空间中选择点(空间更新)来实现。移动类型,即插入或移除,是根据所选点的局部环境推导出来的。二维方阱流体的结果表明,所提出算法的序贯版本比连续流体的标准巨正则算法收敛得更快。由于更新的性质,通过域分解进行并行实现可以进一步减少模拟时间。