A conservative and a hybrid early rejection schemes for accelerating Monte Carlo molecular simulation

Molecular simulation could provide detailed description of fluid systems when compared to experimental techniques. They can also replace equations of state; however, molecular simulation usually costs considerable computational efforts. Several techniques have been developed to overcome such high computational costs. In this paper, two early rejection schemes, a conservative and a hybrid one, are introduced. In these two methods, undesired configurations generated by the Monte Carlo trials are rejected earlier than it would when using conventional algorithms. The methods are tested for structureless single-component Lennard–Jones particles in both canonical and NVT-Gibbs ensembles. The computational time reduction for both ensembles is observed at a wide range of thermodynamic conditions. Results show that computational time savings are directly proportional to the rejection rate of Monte Carlo trials. The proposed conservative scheme has shown to be successful in saving up to 40% of the computational time in the canonical ensemble and up to 30% in the NVT-Gibbs ensemble when compared to standard algorithms. In addition, it preserves the exact Markov chains produced by the Metropolis scheme. Further enhancement for NVT-Gibbs ensemble is achieved by combining this technique with the bond formation early rejection one. The hybrid method achieves more than 50% saving of the central processing unit (CPU) time.

[1]  Ioannis G. Economou,et al.  Influence of simulation protocols on the efficiency of Gibbs ensemble Monte Carlo simulations , 2013 .

[2]  Michael P. Allen,et al.  Computer simulation in chemical physics , 1993 .

[3]  S. Duane,et al.  Hybrid Monte Carlo , 1987 .

[4]  E. Maginn From discovery to data: What must happen for molecular simulation to become a mainstream chemical engineering tool , 2009 .

[5]  D. Frenkel,et al.  Computer simulations in the Gibbs ensemble , 1989 .

[6]  Shuyu Sun,et al.  An Efficient Method of Reweighting and Reconstructing Monte Carlo Molecular Simulation Data for Extrapolation to Different Temperature and Density Conditions , 2013, ICCS.

[7]  A. Panagiotopoulos Direct determination of phase coexistence properties of fluids by Monte Carlo simulation in a new ensemble , 1987 .

[8]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[9]  D. Frenkel Advanced Monte Carlo techniques , 1993 .

[10]  Berend Smit,et al.  Understanding molecular simulation: from algorithms to applications , 1996 .

[11]  J. P. Valleau,et al.  Temperature-and-density-scaling Monte Carlo: isothermal–isobaric thermodynamics of Lennard-Jonesium , 2005 .

[12]  A. Lyubartsev,et al.  New approach to Monte Carlo calculation of the free energy: Method of expanded ensembles , 1992 .

[13]  G. Parisi,et al.  Simulated tempering: a new Monte Carlo scheme , 1992, hep-lat/9205018.

[14]  Wang,et al.  Nonuniversal critical dynamics in Monte Carlo simulations. , 1987, Physical review letters.

[15]  J. Pablo,et al.  Molecular simulations in chemical engineering: Present and future , 2002 .

[16]  G. Torrie,et al.  Monte Carlo study of a phase‐separating liquid mixture by umbrella sampling , 1977 .

[17]  Gordon M. Crippen,et al.  Conformational analysis by energy embedding , 1982 .

[18]  Athanassios Z. Panagiotopoulos,et al.  Phase equilibria by simulation in the Gibbs ensemble , 1988 .

[19]  G. Torrie,et al.  Nonphysical sampling distributions in Monte Carlo free-energy estimation: Umbrella sampling , 1977 .

[20]  J. P. Valleau,et al.  Temperature-and-density-scaling Monte Carlo: methodology and the canonical thermodynamics of Lennard-Jonesium , 2005 .

[21]  Berend Smit,et al.  Simulating the critical behaviour of complex fluids , 1993, Nature.

[22]  J. P. Valleau,et al.  Density‐scaling Monte Carlo study of subcritical Lennard‐Jonesium , 1993 .

[23]  J. P. Valleau,et al.  Density-scaling: a new Monte Carlo technique in statistical mechanics , 1991 .

[24]  J. P. Valleau,et al.  The Coulombic phase transition : density-scaling Monte Carlo , 1991 .

[25]  C. Geyer,et al.  Annealing Markov chain Monte Carlo with applications to ancestral inference , 1995 .

[26]  Robert H. Swendsen,et al.  Erratum: ``New Monte Carlo technique for studying phase transitions'' [Phys. Rev. Lett. 61, 2635 (1988)] , 1989 .