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Volume 24, Issue 3
Computational Software: Polymer Chain Generation for Coarse-Grained Models Using Radical-Like Polymerization

Morgane Mahaud, Zengqiang Zhai, Michel Perez, Olivier Lame, Claudio Fusco, Laurent Chazeau, Ali Makke, Grégory Marque & Julien Morthomas

DOI: 10.4208/cicp.OA-2017-0146

Commun. Comput. Phys., 24 (2018), pp. 885-898.

Published online: 2018-05

[An open-access article; the PDF is free to any online user.]

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  • Abstract

This paper presents major improvements in the efficiency of the so-called Radical-Like Polymerization (RLP) algorithm proposed in "Polymer chain generation for coarse-grained models using radical-like polymerization" [J. Chem. Phys. 128(2008)]. Three enhancements are detailed in this paper: (1) the capture radius of a radical is enlarged to increase the probability of finding a neighboring monomer; (2) between each growth step, equilibration is now performed with increasing the relaxation time depending on the actual chain size; (3) the RLP algorithm is now fully parallelized and proposed as a "fix" within the "Lammps" molecular dynamics simulation suite.

  • Keywords

Molecular dynamics (MD), coarse grained polymer model, LAMMPS, polymer, parallel computing.

  • AMS Subject Headings

82D60

  • Copyright

COPYRIGHT: © Global Science Press

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  • TXT
@Article{CiCP-24-885, author = {Mahaud , MorganeZhai , ZengqiangPerez , MichelLame , OlivierFusco , ClaudioChazeau , LaurentMakke , AliMarque , Grégory and Morthomas , Julien}, title = {Computational Software: Polymer Chain Generation for Coarse-Grained Models Using Radical-Like Polymerization}, journal = {Communications in Computational Physics}, year = {2018}, volume = {24}, number = {3}, pages = {885--898}, abstract = {

This paper presents major improvements in the efficiency of the so-called Radical-Like Polymerization (RLP) algorithm proposed in "Polymer chain generation for coarse-grained models using radical-like polymerization" [J. Chem. Phys. 128(2008)]. Three enhancements are detailed in this paper: (1) the capture radius of a radical is enlarged to increase the probability of finding a neighboring monomer; (2) between each growth step, equilibration is now performed with increasing the relaxation time depending on the actual chain size; (3) the RLP algorithm is now fully parallelized and proposed as a "fix" within the "Lammps" molecular dynamics simulation suite.

}, issn = {1991-7120}, doi = {https://doi.org/10.4208/cicp.OA-2017-0146}, url = {http://global-sci.org/intro/article_detail/cicp/12285.html} }
TY - JOUR T1 - Computational Software: Polymer Chain Generation for Coarse-Grained Models Using Radical-Like Polymerization AU - Mahaud , Morgane AU - Zhai , Zengqiang AU - Perez , Michel AU - Lame , Olivier AU - Fusco , Claudio AU - Chazeau , Laurent AU - Makke , Ali AU - Marque , Grégory AU - Morthomas , Julien JO - Communications in Computational Physics VL - 3 SP - 885 EP - 898 PY - 2018 DA - 2018/05 SN - 24 DO - http://doi.org/10.4208/cicp.OA-2017-0146 UR - https://global-sci.org/intro/article_detail/cicp/12285.html KW - Molecular dynamics (MD), coarse grained polymer model, LAMMPS, polymer, parallel computing. AB -

This paper presents major improvements in the efficiency of the so-called Radical-Like Polymerization (RLP) algorithm proposed in "Polymer chain generation for coarse-grained models using radical-like polymerization" [J. Chem. Phys. 128(2008)]. Three enhancements are detailed in this paper: (1) the capture radius of a radical is enlarged to increase the probability of finding a neighboring monomer; (2) between each growth step, equilibration is now performed with increasing the relaxation time depending on the actual chain size; (3) the RLP algorithm is now fully parallelized and proposed as a "fix" within the "Lammps" molecular dynamics simulation suite.

Morgane Mahaud, Zengqiang Zhai, Michel Perez, Olivier Lame, Claudio Fusco, Laurent Chazeau, Ali Makke, Grégory Marque & Julien Morthomas. (2020). Computational Software: Polymer Chain Generation for Coarse-Grained Models Using Radical-Like Polymerization. Communications in Computational Physics. 24 (3). 885-898. doi:10.4208/cicp.OA-2017-0146
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