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Effectiveness of coarse graining degree and speedup on the dynamic properties of homopolymer

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Abstract

Evaluation of effective coarse graining (CG) degree and reasonable speedup relative to all-atomistic (AA) model was conducted to provide a basis for building appropriate larger-scale model. The reproducibility of atomistic conformation and temperature transferability both act as the analysis criteria to resolve the maximum acceptable CG degree. Taking short- and long time spans into account simultaneously in the estimation of computational speedup, a dynamic scaling factor is accessible in fitting mean squared displacement ratio of CG to AA as an exponential function. Computing loss in parallel running is an indispensable component in acceleration, which was also added in the evaluation. Subsequently, a quantified prediction of CG speedup arises as a multiplication of dynamic scaling factor, computing loss, time step, and the square of reduction in the number of degrees of freedom. Polyethylene oxide was adopted as a reference system to execute the direct Boltzmann inversion and iterative Boltzmann inversion. Bonded and non-bonded potentials were calculated in CG models with 1~4 monomers per bead. The effective CG degree was determined as two at the most with a speedup of four orders magnitude over AA in this study. Determination of effectiveness CG degree and the corresponding speedup prediction provide available tools in larger spatiotemporal-scale calculations.

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References

  1. Gooneie A, Schuschnigg S, Holzer C (2017) A review of multiscale computational methods in polymeric materials. Polymers 9(1):1–80. https://doi.org/10.3390/polym9010016

    Article  CAS  Google Scholar 

  2. Ohkuma T, Kremer K (2017) Comparison of two coarse-grained models of cis-polyisoprene with and without pressure correction. Polymer 130:88–101. https://doi.org/10.1016/j.polymer.2017.09.062

    Article  CAS  Google Scholar 

  3. Noid WG (2013) Systematic methods for structurally consistent coarse-grained models. In: Monticelli L, Salonen E (eds) Biomolecular Simulations: Methods and Protocols. Humana Press, Totowa, pp 487–531. https://doi.org/10.1007/978-1-62703-017-5_19

    Chapter  Google Scholar 

  4. Empereur-mot C, Pesce L, Bochicchio D, Perego C, Pavan GM (2020) Swarm-CG: Automatic parametrization of bonded terms in coarse-grained models of simple to complex molecules via fuzzy self-tuning particle swarm optimization. ChemRxiv. https://doi.org/10.26434/chemrxiv.12613427.v2

  5. Bejagam KK, Singh S, An Y, Deshmukh SA (2018) Machine-learned coarse-grained models. J Phys Chem Lett 9(16):4667–4672. https://doi.org/10.1021/acs.jpclett.8b01416

    Article  CAS  PubMed  Google Scholar 

  6. Reith D, Putz M, Muller-Plathe F (2003) Deriving effective mesoscale potentials from atomistic simulations. J Comput Chem 24(13):1624–1636. https://doi.org/10.1002/jcc.10307

    Article  CAS  PubMed  Google Scholar 

  7. Sovova Z, Berka K, Otyepka M, Jurecka P (2015) Coarse-grain simulations of skin ceramide NS with newly derived parameters clarify structure of melted phase. J Phys Chem B 119(10):3988–3998. https://doi.org/10.1021/jp5092366

    Article  CAS  PubMed  Google Scholar 

  8. Schulze E, Stein M (2018) Simulation of mixed self-assembled monolayers on gold: effect of terminal alkyl anchor chain and monolayer composition. J Phys Chem B 122(31):7699–7710. https://doi.org/10.1021/acs.jpcb.8b05075

    Article  CAS  PubMed  Google Scholar 

  9. Li Y, Kroger M, Liu WK (2011) Primitive chain network study on uncrosslinked and crosslinked cis-polyisoprene polymers. Polymer 52(25):5867–5878. https://doi.org/10.1016/j.polymer.2011.10.044

    Article  CAS  Google Scholar 

  10. Li Y, Tang S, Abberton BC, Kroger M, Burkhart C, Jiang B, Papakonstantopoulos GJ, Poldneff M, Liu WK (2012) A predictive multiscale computational framework for viscoelastic properties of linear polymers. Polymer 53(25):5935–5952. https://doi.org/10.1016/j.polymer.2012.09.055

    Article  CAS  Google Scholar 

  11. Li X, Kou D, Rao S, Liang H (2006) Developing a coarse-grained force field for the diblock copolymer poly (styrene-b-butadiene) from atomistic simulation. J Chem Phys 124(20):204909. https://doi.org/10.1063/1.2200694

    Article  CAS  PubMed  Google Scholar 

  12. Tschop W, Kremer K, Batoulis J, Burger T, Hahn O (1998) Simulation of polymer melts. I. Coarse-graining procedure for polycarbonates. Acta Polym 49(2-3):61–74. https://doi.org/10.1002/(sici)1521-4044(199802)49:2/3<61::aid-apol61>3.0.co;2-v

    Article  Google Scholar 

  13. Agrawal V, Peralta P, Li YY, Oswald J (2016) A pressure-transferable coarse-grained potential for modeling the shock Hugoniot of polyethylene. J Chem Phys 145(10). https://doi.org/10.1063/1.4962255

  14. Moore TC, Iacovella CR, McCabe C (2014) Derivation of coarse-grained potentials via multistate iterative Boltzmann inversion. J Chem Phys 140(22). https://doi.org/10.1063/1.4880555

  15. Müller M, JJd P (2013) Computational approaches for the dynamics of structure formation in self-assembling polymeric materials. Annu Rev Mater Res 43(1):1–34. https://doi.org/10.1146/annurev-matsci-071312-121618

    Article  CAS  Google Scholar 

  16. Salerno KM, Agrawal A, Perahia D, Grest GS (2016) Resolving dynamic properties of polymers through coarse-grained computational studies. Phys Rev Lett 116(5). https://doi.org/10.1103/PhysRevLett.116.058302

  17. Salerno KM, Agrawal A, Peters BL, Perahia D, Grest GS (2016) Dynamics in entangled polyethylene melts. Eur Phys J-Spec Top 225(8-9):1707–1722. https://doi.org/10.1140/epjst/e2016-60142-7

    Article  CAS  Google Scholar 

  18. Depa P, Chen CX, Maranas JK (2011) Why are coarse-grained force fields too fast? A look at dynamics of four coarse-grained polymers. J Chem Phys 134(1). https://doi.org/10.1063/1.3513365

  19. Depa PK, Maranas JK (2005) Speed up of dynamic observables in coarse-grained molecular-dynamics simulations of unentangled polymers. J Chem Phys 123(9):94901. https://doi.org/10.1063/1.1997150

    Article  CAS  PubMed  Google Scholar 

  20. Lee H, de Vries AH, Marrink S-J, Pastor RW (2009) A coarse-grained model for polyethylene oxide and polyethylene glycol: conformation and hydrodynamics. J Phys Chem B 113(40):13186–13194. https://doi.org/10.1021/jp9058966

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Nawaz S, Carbone P (2014) Coarse-Graining Poly (ethylene oxide)–Poly (propylene oxide)–Poly(ethylene oxide) (PEO–PPO–PEO) Block copolymers using the MARTINI force field. J Phys Chem B 118(6):1648–1659. https://doi.org/10.1021/jp4092249

    Article  CAS  PubMed  Google Scholar 

  22. Wu C (2019) Bulk modulus of poly(ethylene oxide) simulated using the systematically coarse-grained model. Comput Mater Sci 156:89–95. https://doi.org/10.1016/j.commatsci.2018.09.043

    Article  CAS  Google Scholar 

  23. Brini E, Algaer EA, Ganguly P, Li C, Rodriguez-Ropero F, van der Vegt NFA (2013) Systematic coarse-graining methods for soft matter simulations - a review. Soft Matter 9(7):2108–2119. https://doi.org/10.1039/C2SM27201F

    Article  CAS  Google Scholar 

  24. Lee H, Venable RM, MacKerell AD, Pastor RW (2008) Molecular dynamics studies of polyethylene oxide and polyethylene glycol: hydrodynamic radius and shape anisotropy. Biophys J 95(4):1590–1599. https://doi.org/10.1529/biophysj.108.133025

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Martínez L, Andrade R, Birgin EG, Martínez JM (2009) PACKMOL: a package for building initial configurations for molecular dynamics simulations. J Comput Chem 30(13):2157–2164. https://doi.org/10.1002/jcc.21224

    Article  CAS  PubMed  Google Scholar 

  26. Agrawal V, Arya G, Oswald J (2014) Simultaneous iterative Boltzmann inversion for coarse-graining of polyurea. Macromolecules 47(10):3378–3389. https://doi.org/10.1021/ma500320n

    Article  CAS  Google Scholar 

  27. Plimpton S (1995) Fast parallel algorithms for short-range molecular dynamics. J Comput Phys 117:1–19

    Article  CAS  Google Scholar 

  28. Ruhle V, Junghans C, Lukyanov A, Kremer K, Andrienko D (2009) Versatile object-oriented toolkit for coarse-graining applications. J Chem Theory Comput 5(12):3211–3223. https://doi.org/10.1021/ct900369w

    Article  CAS  PubMed  Google Scholar 

  29. Humphrey W, Dalke A, Schulten K (1996) VMD: visual molecular dynamics. J Mol Graph Model 14(1):33–38

    Article  CAS  Google Scholar 

  30. Alexander S (2010) Visualization and analysis of atomistic simulation data with OVITO–the open visualization tool. Model Simul Mater Sc 18(1):015012. https://doi.org/10.1088/0965-0393/18/1/015012

    Article  Google Scholar 

  31. van Zon A, Mos B, Verkerk P, de Leeuw SW (2001) On the dynamics of PEO-NaI polymer electrolytes. Electrochim Acta 46(10):1717–1721. https://doi.org/10.1016/S0013-4686(00)00776-3

    Article  Google Scholar 

  32. Wang QF, Keffer DJ, Nicholson DM (2011) A coarse-grained model for polyethylene glycol polymer. J Chem Phys 135(21). https://doi.org/10.1063/1.3664623

  33. Choi E, Mondal J, Yethiraj A (2014) Coarse-grained models for aqueous polyethylene glycol solutions. J Phys Chem B 118(1):323–329. https://doi.org/10.1021/jp408392b

    Article  CAS  PubMed  Google Scholar 

  34. Taddese T, Carbone P (2017) Effect of chain length on the partition properties of (polyethylene oxide): comparison between MARTINI coarse-grained and atomistic models. J Phys Chem B 121(7):1601–1609. https://doi.org/10.1021/acs.jpcb.6b10858

    Article  CAS  PubMed  Google Scholar 

  35. Wu C (2018) Multiscale modeling scheme for simulating polymeric melts: application to poly(ethylene oxide). Macromol Theory Simul 27(1). https://doi.org/10.1002/mats.201700066

  36. Chen CX, Depa P, Sakai VG, Maranas JK, Lynn JW, Peral I, Copley JRD (2006) A comparison of united atom, explicit atom, and coarse-grained simulation models for poly(ethylene oxide). J Chem Phys 124(23):234901. https://doi.org/10.1063/1.2204035

    Article  CAS  PubMed  Google Scholar 

  37. Cordeiro RM, Zschunke F, Mueller-Plathe F (2010) Mesoscale molecular dynamics simulations of the force between surfaces with grafted poly(ethylene oxide) chains derived from atomistic simulations. Macromolecules 43(3):1583–1591. https://doi.org/10.1021/ma902060k

    Article  CAS  Google Scholar 

  38. Fischer J, Paschek D, Geiger A, Sadowski G (2008) Modeling of aqueous poly(oxyethylene) solutions: 1. Atomistic Simulations. J Phys Chem B 112(8):2388–2398. https://doi.org/10.1021/jp0765345

    Article  CAS  PubMed  Google Scholar 

  39. Chen QP, Xie S, Foudazi R, Lodge TP, Siepmann JI (2018) Understanding the molecular weight dependence of χ and the effect of dispersity on polymer blend phase diagrams. Macromolecules 51(10):3774–3787. https://doi.org/10.1021/acs.macromol.8b00604

    Article  CAS  Google Scholar 

  40. Stubbs JM, Potoff JJ, Siepmann JI (2004) Transferable potentials for phase equilibria. 6. United-Atom Description for Ethers, Glycols, Ketones, and Aldehydes. J Phys Chem B 108(45):17596–17605. https://doi.org/10.1021/jp049459w

    Article  CAS  Google Scholar 

  41. Glotzer SC, Paul W (2002) Molecular and mesoscale simulation methods for polymer materials. Annu Rev Mater Res 32:401–436. https://doi.org/10.1146/annurev.matsci.32.010802.112213

    Article  CAS  Google Scholar 

  42. Bayramoglu B, Faller R (2013) Modeling of polystyrene under confinement: exploring the limits of iterative Boltzmann inversion. Macromolecules 46(19):7957–7976. https://doi.org/10.1021/ma400831g

    Article  CAS  Google Scholar 

  43. Huang H, Wu L, Xiong H, Sun H (2019) A transferrable coarse-grained force field for simulations of polyethers and polyether blends. Macromolecules 52(1):249–261. https://doi.org/10.1021/acs.macromol.8b01802

    Article  CAS  Google Scholar 

  44. Xie ZM, Chai DL, Wang YS, Tan HF (2016) Directly modifying the nonbonded potential based on the standard iterative boltzmann inversion method for coarse-grained force fields. J Phys Chem B 120(45):11834–11844. https://doi.org/10.1021/acs.jpcb.6b06457

    Article  CAS  PubMed  Google Scholar 

  45. Enns JB, Simha R (2006) Transitions in semicrystalline polymers. II. Polyoxymethylene and poly(ethylene oxide). J Macromol Sci B 13(1):25–47. https://doi.org/10.1080/00222347708208751

    Article  Google Scholar 

  46. Iwaoka N, Hagita K, Takano H (2018) Multipoint segmental repulsive potential for entangled polymer simulations with dissipative particle dynamics. J Chem Phys 149:114901. https://doi.org/10.1063/1.5046755

    Article  CAS  PubMed  Google Scholar 

  47. Sirk TW, Slizoberg YR, Brennan JK, Lisal M, Andzelm JW (2012) An enhanced entangled polymer model for dissipative particle dynamics. J Chem Phys 136(13):134903. https://doi.org/10.1063/1.3698476

    Article  CAS  PubMed  Google Scholar 

  48. Niedzwiedz K, Wischnewski A, Pyckhout-Hintzen W, Allgaier J, Richter D, Faraone A (2008) Chain dynamics and viscoelastic properties of poly(ethylene oxide). Macromolecules 41:4866–4872. https://doi.org/10.1021/ma800446n

    Article  CAS  Google Scholar 

  49. Peters BL, Salerno KM, Agrawal A, Perahia D, Grest GS (2017) Coarse grained modeling of polyethylene melts: effect on dynamics. J Chem Theory Comput 13(6):2890–2896. https://doi.org/10.1021/acs.jctc.7b00241

    Article  CAS  PubMed  Google Scholar 

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Acknowledgments

The parallel computing was supported by the National Supercomputing Center in Shenzhen (Shenzhen Cloud Computing Center) and the Computing Facility, Institute of Mechanics, Chinese Academy of Sciences.

Funding

This study was funded by the National Natural Science Foundation of China (Project No. 11672314).

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Conceptualization: Lijuan Liao; Methodology: Lijuan Liao and Changyu Meng; Formal analysis and investigation: Lijuan Liao and Changyu Meng; Writing—original draft preparation: Lijuan Liao; Funding acquisition: Lijuan Liao; Supervision: Chenguang Huang.

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Liao, L., Meng, C. & Huang, C. Effectiveness of coarse graining degree and speedup on the dynamic properties of homopolymer. J Mol Model 27, 55 (2021). https://doi.org/10.1007/s00894-020-04661-5

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