Persistence Length, End-to-End Distance, and Structure of Coarse-Grained Polymers

Coarse-grained (CG)
polymer simulations can access longer times
and larger lengths than all-atom (AA) molecular dynamics simulations;
however, not all CG models correctly reproduce polymer properties
on all length scales. Here we coarse-grain atomistic position data
from polyethylene (PE) and polytetrafluoroethylene (PTFE) melt simulations
by combining λ backbone carbon atoms in a single CG bead. Resulting
CG variables have correlations along the chain backbone that depend
on the coarse-graining scale λ and are generally not reproduced
by independent bond-length, bond-angle and torsion-angle distributions.
By constructing distributions of CG variables equivalent to those
from simulated CG potentials we are able to evaluate the bond-orientation
correlation for different CG models at reduced computational cost.
CG models and potentials that include only nonbonded, bond-length,
and bond-angle interactions computed by Boltzmann inversion correctly
reproduce the CG variable distributions but do not necessarily reproduce
the chain stiffness, overestimating the persistence length Lp and end-to-end distance ⟨R2⟩1/2 with increasing λ. While
CG models that include an independent torsion angle match the bond-orientation
correlation and ⟨R2⟩1/2 better, only approximate models that include correlations
between bond and torsion angles match the true bond-orientation correlation.

粗粒度（Coarse-grained, CG）高分子模拟相较于全原子（all-atom, AA）分子动力学模拟，可覆盖更长的时间尺度与更大的空间尺度。然而，并非所有CG模型都能在所有长度尺度下准确复现高分子的物理特性。本研究通过将聚乙烯（polyethylene, PE）与聚四氟乙烯（polytetrafluoroethylene, PTFE）熔体模拟中的λ个主链碳原子合并为单个CG珠子，对原子级位置数据开展粗粒化处理。所得CG变量沿高分子主链的关联特性取决于粗粒化尺度λ，且这类关联通常无法通过独立的键长、键角及二面角分布复现。通过构建与模拟CG势能等价的CG变量分布，我们能够以更低的计算成本评估不同CG模型的键取向关联特性。仅通过玻尔兹曼反演计算非键相互作用、键长与键角相互作用的CG模型及势能，虽可准确复现CG变量分布，但未必能复现高分子链的刚性：随着λ增大，这类模型会高估持久长度Lp与均方根末端距⟨R²⟩^(1/2)。引入独立二面角项的CG模型虽能更好地匹配键取向关联与⟨R²⟩^(1/2)，但唯有纳入键与二面角之间关联的近似模型，才能准确复现真实的键取向关联。