Genetic Algorithm for Discovery of Globally Stable Phases in Block Copolymers
Genetic Algorithm for Discovery of Globally Stable Phases in Block Copolymers
复制标题
用于发现嵌段共聚物中全局稳定相的遗传算法
DOI：
10.1021/acs.macromol.6b01323
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发表时间：
2016
期刊：
影响因子：
5.5
通讯作者：
G. Fredrickson
中科院分区：
文献类型：
--
作者：
Carol L. Tsai;K. Delaney;G. Fredrickson
描述了一种利用遗传算法（GA）结合自洽场理论（SCFT）组成的框架确定嵌段共聚物全局稳定形貌的方法。我们介绍了这种GA-SCFT技术在典型AB二嵌段共聚物熔体上的基准测试结果，其理论相图早已建立。GA-SCFT算法成功地预测了大型三维模拟细胞（包括HEX、LAM和BCC）在宽组成范围和弱至中等偏析强度下的许多常规中间相。然而，GA-SCFT方法目前在恢复网络相（如陀螺结构）或在强偏析极限下的BCC相方面并不有效。这些挑战可能部分归因于在大型模拟细胞中看到的大密度缺陷态和较高的偏析强度。
A method of determining the globally stable morphologies of block copolymers using a framework composed of a genetic algorithm (GA) in conjunction with self-consistent field theory (SCFT) is described. We present results from benchmark testing of this GA-SCFT technique on the canonical AB diblock copolymer melt, for which the theoretical phase diagram has long been established. The GA-SCFT algorithm successfully predicts many of the conventional mesophases from random initial conditions in large, three-dimensional simulation cells, including HEX, LAM, and BCC, over a broad composition range and weak to moderate segregation strength. However, the GA-SCFT method is currently not effective at recovery of network phases, such as the gyroid structure, or for the BCC phase in the strong segregation limit. These challenges may be partially attributed to the large density of defective states seen in large simulation cells and at higher segregation strengths.