We introduce the Propagator-Biased Chain Generation (PBCG) algorithm, which generates initial configurations for coarse-grained molecular dynamics simulations of block copolymers presenting microphase separation. We build on the classical self-consistent field theory (SCFT) and show how its main statistical objects, the so-called forward and backward chain propagators, can be properly utilized to bias the configuration of coarse-grained bead–spring chains. Both the local volume fractions and the spatial segment distributions predicted by SCFT are accurately reproduced by configurations yielded by the algorithm. The PBCG algorithm supports the multiscale approach by allowing simulations to start in a state that is very close to the phase-separated equilibrium, typically much harder to obtain when starting from a random initial state. We demonstrate how to apply the algorithm to generic coarse-grained systems in reduced units as well as to chemically specific models of materials such as styrene-isoprene-styrene triblock copolymers.

中文翻译：

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传播器偏置链生成：精确逆向映射微相分离的嵌段共聚物


我们介绍了传播器偏置链生成 （PBCG） 算法，该算法为呈现微相分离的嵌段共聚物的粗粒度分子动力学模拟生成初始配置。我们建立在经典的自洽场论 （SCFT） 之上，展示了如何正确利用其主要统计对象，即所谓的正向和反向链传播器，来偏置粗粒度珠子-弹簧链的配置。SCFT 预测的局部体积分数和空间段分布都由算法产生的配置准确再现。PBCG 算法支持多尺度方法，允许仿真从非常接近相分离平衡的状态开始，而从随机初始状态开始时通常更难获得。我们演示了如何将该算法应用于简化单元的通用粗晶系统，以及苯乙烯-异戊二烯-苯乙烯三嵌段共聚物等材料的化学特异性模型。