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Articles

DL_MONTE: a multipurpose code for Monte Carlo simulation

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Pages 131-151 | Received 16 Feb 2018, Accepted 07 Jan 2019, Published online: 01 Feb 2019

References

  • Allen MP, Tildesley DJ. Computer simulation of liquids. Oxford: Oxford University Press; 1987.
  • Frenkel D, Smit B. Understanding molecular simulation: from algorithms to applications. London: Academic Press; 2002.
  • Marc Meunier. Industrial applications of molecular simulations. Boca Raton: Taylor & Francis Group, CRC Press; 2012.
  • Knight JF, Monson PA. Computer simulation of adsorption equilibrium for a gas on a solid surface using the potential distribution theory. J Chem Phys. 1986;84:1909. doi: 10.1063/1.450440
  • Fan C, Do DD, Nicholson D. New Monte Carlo simulation of adsorption of gases on surfaces and in pores: a concept of multibins. J Phys Chem B. 2011;115:10509. doi: 10.1021/jp205497s
  • Gatica SM, Kostov MK, Cole MW. Ordering transition of gases adsorbed on a C60 surface: Monte Carlo simulations and lattice-gas models. Phys Rev B. 2008;78:205417. doi: 10.1103/PhysRevB.78.205417
  • Wilding NB. Computer simulation of fluid phase transitions. Am J Phys. 2001;69(11):1147. doi: 10.1119/1.1399044
  • Brukhno AV, Åkesson T, Jönsson B. Phase behavior in suspensions of highly charged colloids. J Phys Chem B. 2009;113(19):6766. doi: 10.1021/jp811147v
  • Brukhno AV, Akinshina A, Zachary A, et al. Phase phenomena in supported lipid films under varying electric potential. Soft Matter. 2011;7:1006. doi: 10.1039/C0SM00724B
  • Cracknel RF, Nicholson D. Adsorption of gas mixtures on solid surfaces, theory and computer simulation. Adsorption. 1995;1:7. doi: 10.1007/BF00704142
  • Lima AP, Martins AS, Sa Martinset JS. Physica A: statistical mechanics and its applications. Physica A. 2012;391:4281. doi: 10.1016/j.physa.2012.04.003
  • Panagiotopoulos A. Direct determination of phase coexistence properties of fluids by Monte Carlo simulation in a new ensemble. Mol Phys. 1987;61:813. doi: 10.1080/00268978700101491
  • Bruce AD, Wilding NB, Ackland GJ. Free energy of crystalline solids: a lattice-switch Monte Carlo method. Phys Rev Lett. 1997;79(16):3002. doi: 10.1103/PhysRevLett.79.3002
  • Swendsen RH, Wang JS. Replica Monte Carlo simulation of spin-glasses. Phys Rev Let. 1986;57:2607. doi: 10.1103/PhysRevLett.57.2607
  • Earl DJ, Deem MW. Parallel tempering: theory, applications, and new perspectives. Phys Chem Chem Phys. 2005;7:3910. doi: 10.1039/b509983h
  • Torrie GM, Valleau JP. Monte Carlo free energy estimates using non-Boltzmann sampling: application to the sub-critical Lennard-Jones fluid. Chem Phys Lett. 1974;28:578. doi: 10.1016/0009-2614(74)80109-0
  • Torrie GM, Valleau JP. Nonphysical sampling distributions in Monte Carlo free-energy estimation: umbrella sampling. J Comp Phys. 1977;23:187. doi: 10.1016/0021-9991(77)90121-8
  • Mezei M. Adaptive umbrella sampling: self-consistent determination of the non-Boltzmann bias. J Comp Phys. 1987;68:237. doi: 10.1016/0021-9991(87)90054-4
  • Mezei M. Evaluation of the adaptive umbrella sampling method. Mol Sim. 1989;3:301. doi: 10.1080/08927028908031382
  • Lyubartsev AP, Martsinovskii AA, Shevkunov CV, et al. New approach to Monte Carlo calculation of the free energy: method of expanded ensembles. J Chem Phys. 1992;96:1776. doi: 10.1063/1.462133
  • Broukhno AV, Jönsson B, Åkesson T, et al. Depletion and bridging forces in polymer systems: Monte Carlo simulations at constant chemical potential. J Chem Phys. 2000;113:5493. doi: 10.1063/1.1289821
  • Hansmann UHE, Okamoto Y. Generalized-ensemble Monte Carlo method for systems with rough energy landscape. Phys Rev E. 1997;56:2228. doi: 10.1103/PhysRevE.56.2228
  • Iba Y. Extended ensemble Monte Carlo. Int J Mod Phys C. 2001;12:623. doi: 10.1142/S0129183101001912
  • Lee J. New Monte Carlo algorithm: entropic sampling. Phys Rev Lett. 1993;71:2353. doi: 10.1103/PhysRevLett.71.2353.2
  • Wang F, Landau DP. Efficient, multiple-range random walk algorithm to calculate the density of states. Phys Rev Lett. 2001;86:2050. doi: 10.1103/PhysRevLett.86.2050
  • Smith GR, Bruce AD. A study of the multi-canonical Monte Carlo method. J Phys A Math Gen. 1995;28:623. doi: 10.1088/0305-4470/28/23/015
  • Fitzgerald M, Picard RR, Silver RN. Canonical transition probabilities for adaptive metropolis simulation. Europhys Lett. 1999;46:282. doi: 10.1209/epl/i1999-00257-1
  • Smith W, Forester TJ. DL_POLY_2.0: a general-purpose parallel molecular dynamics simulation package. Molec Graphics. 1996;14:136. doi: 10.1016/S0263-7855(96)00043-4
  • Todorov IT, Smith W, Trachenko K, et al. DL_POLY_3: new dimensions in molecular dynamics simulations via massive parallelism. J Mater Chem. 2006;16:1911. doi: 10.1039/b517931a
  • Shah JK, Marin-Rimoldi E, Gotchy Mullen R, et al. Cassandra: an open source Monte Carlo package for molecular simulation. J Comput Chem. 2017;38:1727. doi: 10.1002/jcc.24807
  • Gupta A, Chempath S, Sanborn MJ, et al. Object-oriented programming paradigms for molecular modeling. Mol Sim. 2003;29:29. doi: 10.1080/0892702031000065719
  • Dubbeldam D, Calero S, Ellis DE, et al. RASPA: molecular simulation software for adsorption and diffusion in flexible nanoporous materials. Mol Sim. 2016;42:81. doi: 10.1080/08927022.2015.1010082
  • Martin MG. MCCCS Towhee: a tool for Monte Carlo molecular simulation. Mol Sim. 2013;39:1212. doi: 10.1080/08927022.2013.828208
  • Purton JA, Crabtree JC, Parker SC. DL_MONTE: a general purpose program for parallel Monte Carlo simulation. Mol Sim. 2013;39:1240. doi: 10.1080/08927022.2013.839871
  • Available from: http://www.epsrc.ac.uk
  • Available from: http://www.ccp5.ac.uk
  • Ferrenberg AM, Swendsen RH. New Monte Carlo technique for studying phase transitions. Phys Rev Lett. 1988;61:2635. doi: 10.1103/PhysRevLett.61.2635
  • Ferrenberg AM, Swendsen RH. Optimized Monte Carlo data analysis. Phys Rev Lett. 1989;63:1195. doi: 10.1103/PhysRevLett.63.1195
  • Siepmann JI, Frenkel D. Configurational bias Monte Carlo: a new sampling scheme for flexible chains. Mol Phys. 1992;75:59. doi: 10.1080/00268979200100061
  • Dress C, Krauth W. Cluster algorithm for hard spheres and related systems. J Phys A. 1995;28:L597. doi: 10.1088/0305-4470/28/23/001
  • Liu J, Luijten E. Generalized geometric cluster algorithm for fluid simulation. Phys Rev E. 2005;71:066701.
  • Torrie GM, Valleau JP. Electrical double layers. I. Monte Carlo study of a uniformly charged surface. J Chem Phys. 1980;73:5807. doi: 10.1063/1.440065
  • Valleau JP, Ivkov R, Torrie GM. Colloid stability: the forces between charged surfaces in an electrolyte. J Chem Phys. 1991;95:520. doi: 10.1063/1.461452
  • Broukhno AV, Khan MO, Åkesson T, et al. Polyampholyte-induced repulsion between charged surfaces: Monte Carlo simulation studies. Langmuir. 2002;18(16):6429. doi: 10.1021/la020094l
  • Yeh I, Berkowitz ML. Ewald summation for systems with slab geometry. J Chem Phys. 1999;111:3155. doi: 10.1063/1.479595
  • Tieleman DP, Hess B, Sansom MSP. Analysis and evaluation of channel models: simulations of alamethicin. Biophys J. 2002;83:2393. doi: 10.1016/S0006-3495(02)75253-3
  • Bostick D, Berkowitz ML. The implementation of slab geometry for membrane-channel molecular dynamics simulations. Biophys J. 2003;85(1):97. doi: 10.1016/S0006-3495(03)74458-0
  • Humphrey W, Dalke A, Schulten K. VMD: visual molecular dynamics. J Mol Graphics. 1996;14:33. doi: 10.1016/0263-7855(96)00018-5
  • Seeber M, Felline A, Raimondi F, et al. Wordom: a user-friendly program for the analysis of molecular structures, trajectories, and free energy surfaces. J Comput Chem. 2011;6:1183. doi: 10.1002/jcc.21688
  • Berendsen HJC, Grigera JR, Straatsma TP. The missing term in effective pair potentials. J Phys Chem. 1987;91:6269. doi: 10.1021/j100308a038
  • Bruce AD, Wilding NB. Computational strategies for mapping equilibrium phase diagrams. Adv Chem Phys. 2004;127:1.
  • Kästner J. Umbrella sampling. Comput Mol Sci. 2011;1:932. doi: 10.1002/wcms.66
  • Shirts MR, Chodera JD. Statistically optimal analysis of samples from multiple equilibrium states. J Chem Phys. 2008;129:124105.
  • Kumar S, Rosenberg JM, Bouzida D, et al. THE weighted histogram analysis method for free-energy calculations on biomolecules. I. The method. J Comput Chem. 1992;13:1011. doi: 10.1002/jcc.540130812
  • Souaille M, Roux B. Extension to the weighted histogram analysis method: combining umbrella sampling with free energy calculations. Comput Phys Commun. 2001;135:40. doi: 10.1016/S0010-4655(00)00215-0
  • Brukhno AV, Anwar J, Davidchack R, et al. Challenges in molecular simulation of homogeneous ice nucleation. J Phys Condens Matter. 2009;20:494243.
  • Tian P, Jónsson S, Ferkinghoff-Borg J, et al. Robust estimation of diffusion-optimized ensembles for enhanced sampling. J Chem Theory Comput. 2014;10(2):543. doi: 10.1021/ct400844x
  • Berg B, Neuhaus T. Multicanonical ensemble: a new approach to simulate first-order phase transitions. Phys Rev Lett.. 1992;68(1):9. doi: 10.1103/PhysRevLett.68.9
  • Marinari E, Parisi G. Simulated tempering: a new Monte Carlo scheme. Europhys Lett. 1992;19:451. doi: 10.1209/0295-5075/19/6/002
  • Wang JS. Flat histogram Monte Carlo method. Physica A. 2000;281:147. doi: 10.1016/S0378-4371(00)00016-9
  • Bruce AD, Jackson AN, Ackland GJ, et al. Lattice-switch Monte Carlo method. Phys Rev E. 2000;61(1):906. doi: 10.1103/PhysRevE.61.906
  • Underwood TL, Ackland GJ. monteswitch : a package for evaluating solid–solid free energy differences via lattice-switch Monte Carlo. Comput Phys Commun. 2017;216:204. doi: 10.1016/j.cpc.2017.02.011
  • Mendelev MI, Underwood TL, Ackland GJ. Development of an interatomic potential for the simulation of defects, plasticity, and phase transformations in titanium. J Chem Phys. 2016;145(15):154102. doi: 10.1063/1.4964654
  • Quigley D. Communication: thermodynamics of stacking disorder in ice nuclei. J Chem Phys. 2014;141(12):121101. doi: 10.1063/1.4896376
  • Marechal M, Dijstra M. Stability of orientationally disordered crystal structures of colloidal hard dumbbells. Phys Rev E. 2008;77:061405. doi: 10.1103/PhysRevE.77.061405
  • Raiteri P, Gale JD, Quigley D. Derivation of an accurate force-field for simulating the growth of calcium carbonate from aqueous solution: a new model for the calcite–water interface. J Phys Chem C. 2010;114(13):5997. doi: 10.1021/jp910977a
  • Bridgwater S, Quigley D. Lattice-switching Monte Carlo method for crystals of flexible molecules. Phys Rev E. 2014;90:063313. doi: 10.1103/PhysRevE.90.063313
  • Abascal JLF, Vega C. A general purpose model for the condensed phases of water: TIP4P/2005. J Chem Phys. 2005;123:234505. doi: 10.1063/1.2121687
  • Aragones JL, Vega C. Plastic crystal phases of simple water models. J Chem Phys. 2009;130:244504. doi: 10.1063/1.3156856
  • Arnarez CA, Uusitalo JJ, Masman MF, et al. Dry Martini, a coarse-grained force field for lipid membrane simulations with implicit solvent. J Chem Theory Comput. 2015;11:260. doi: 10.1021/ct500477k
  • Hess H, Kutzner C, van der Spoel D, et al. GROMACS 4: algorithms for highly efficient, load-balanced, and scalable molecular simulation. J Chem Theory Comput. 2008;4:435. doi: 10.1021/ct700301q
  • Wu EL, Cheng X, Jo S, et al. CHARMM-GUI membrane builder toward realistic biological membrane simulations. J Comput Chem. 2014;35:1997. doi: 10.1002/jcc.23702
  • Berger O, Edholm O, Jähnig F. Molecular dynamics simulations of a fluid bilayer of dipalmitoylphosphatidylcholine at full hydration, constant pressure, and constant temperature. Biophys J. 1997;72:2002. doi: 10.1016/S0006-3495(97)78845-3
  • Siu SW, Vcha R, Jungwirth P, et al. Biomolecular simulations of membranes: physical properties from different force fields. J Chem Phys. 2008;128:125103. doi: 10.1063/1.2897760
  • Available from: http://www.scarf.rl.ac.uk
  • Akinshina A, Dasb C, Noro MG. Effect of monoglycerides and fatty acids on a ceramide bilayer. Phys Chem Chem Phys. 2016;18:17446. doi: 10.1039/C6CP01238H
  • Jambeck JPM, Lyubartsev AP. Exploring the free energy landscape of solutes embedded in lipid bilayers. J Phys Chem Lett. 2013;4:1781. doi: 10.1021/jz4007993
  • Huang K, Garca AE. Free energy of translocating an arginine-rich cell-penetrating peptide across a lipid bilayer suggests pore formation. Biophys J. 2013;104(2):412. doi: 10.1016/j.bpj.2012.10.027
  • Lyu Y, Xiang N, Zhu X, et al. Potential of mean force for insertion of antimicrobial peptide melittin into a pore in mixed DOPC/DOPG lipid bilayer by molecular dynamics simulation. J Chem Phys. 2017;146:155101. doi: 10.1063/1.4979613
  • Kirkwood JG. Statistical mechanics of fluid mixtures. J Chem Phys. 1935;3:300. doi: 10.1063/1.1749657
  • Underwood TL, Ackland GJ. Lattice-switch Monte Carlo: the fcc—bcc problem. J Phys Conf Ser. 2015;640:012030. doi: 10.1088/1742-6596/640/1/012030
  • Gowers RJ, Farmahini AH, Friedrich D, et al. Automated analysis and benchmarking of GCMC simulation programs in application to gas adsorption. Mol Sim. 2018;44:309. doi: 10.1080/08927022.2017.1375492

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