Simu-D: A Simulator-Descriptor Suite for Polymer-Based Systems under Extreme Conditions

Abstract

We present Simu-D, a software suite for the simulation and successive identification of local structures of atomistic systems, based on polymers, under extreme conditions, in the bulk, on surfaces, and at interfaces. The protocol is built around various types of Monte Carlo algorithms, which include localized, chain-connectivity-altering, identity-exchange, and cluster-based moves. The approach focuses on alleviating one of the main disadvantages of Monte Carlo algorithms, which is the general applicability under a wide range of conditions. Present applications include polymer-based nanocomposites with nanofillers in the form of cylinders and spheres of varied concentration and size, extremely confined and maximally packed assemblies in two and three dimensions, and terminally grafted macromolecules. The main simulator is accompanied by a descriptor that identifies the similarity of computer-generated configurations with respect to reference crystals in two or three dimensions. The Simu-D simulator-descriptor can be an especially useful tool in the modeling studies of the entropy- and energy-driven phase transition, adsorption, and self-organization of polymer-based systems under a variety of conditions.

Polymorphism and Perfection in Crystallization of Hard Sphere Polymers

“…The simulations are conducted using the Simu-D simulator-descriptor suite [91] in the isochoric, semigrand ensemble [VTN sites µ * ]. In the [VTN sites µ * ] ensemble [97] underlying the present calculation, chain lengths obey a given distribution, which is enforced by means of the chemical potentials µ * , and are allowed to fluctuate within a predetermined range.…”

“…The following attempt probabilities for each move have been used: (i) rotation (10%), (ii) reptation (10%), (iii) flip (34.8%), (iv) intermolecular reptation (25%), (v) configurational bias (20%), (vi) simplified end-bridging, sEB (0.1%) and (vii) simplified intermolecular end-bridging, sIEB (0.1%). Neither cluster [102] nor identity exchange moves [91] are incorporated here.…”

“…The initial configuration is generated through progressive shrinkage of a very dilute configuration that meets the constraints imposed by chain connectivity until a desired packing density (volume fraction) of φ = 0.56 is reached [91], or φ * = φ 0.7405 = 0.756 relative to the maximum compacity of the FCC or HCP crystals. The cubic simulation cell is subjected to periodic boundary conditions in all directions corresponding to a bulk and unconstraint system.…”

“…The cubic simulation cell is subjected to periodic boundary conditions in all directions corresponding to a bulk and unconstraint system. Due to the high density a configurational MC bias pattern is used throughout with n dis = 50 candidate configurations being attempted per local move as explained in detail in [91,101].…”

“…In spite of these difficulties, a wide variety of increasingly more efficient MC methods have been developed over the last decades [80,81,[84][85][86][87][88][89][90]. The work to be reported in this manuscript is based on a powerful suite of advanced MC moves [91] which has enabled us in the past to observe the entropy-driven athermal polymer crystallization for the first time [73] and to identify and analyze the factors that affect the phenomenon, including chain length and its distribution [73,92], the presence of bond gaps or tangency [93] and confinement in one [94] or all three [95] dimensions.…”