Inverse Design of Bulk Morphologies in Multiblock Polymers Using Particle Swarm Optimization
Inverse Design of Bulk Morphologies in Multiblock Polymers Using Particle Swarm Optimization
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DOI：
10.1021/acs.macromol.7b01204
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发表时间：
2017-09-12
期刊：
影响因子：
5.5
通讯作者：
Fredrickson, Glenn H.
中科院分区：
文献类型：
--
作者：
Khadilkar, Mihir R.;Paradiso, Sean;Fredrickson, Glenn H.
多嵌段聚合物是一类非常有吸引力的软质材料，因为它们可以在纳米尺度下自发自组装成各种有序的中间相。然而，由于大量的设计参数，包括单体种类、嵌段序列、嵌段分子量和分散性、聚合物结构和二元相互作用参数的选择，控制有序性和因此的性质是困难的。为了找到目标属性的最佳配方，在这个设计空间中导航需要一种以自动化方式有效搜索无数参数组合的方法。我们报告的逆设计策略，目标散装形态利用粒子群优化（PSO）作为一个全局优化和自洽场理论（SCFT）作为一个向前预测引擎。为了避免亚稳态的前向预测中，我们利用伪光谱可变单元SCFT从已知的嵌段共聚物形态的无缺陷种子库开始。我们证明，我们的方法允许一个强大的识别自组装成一个目标结构的嵌段共聚物和共聚物合金，优化参数，如嵌段分数，共混分数，和Flory chi参数。
Multiblock polymers are a fascinating class of soft materials owing to their spontaneous self-assembly into a variety of ordered mesophases at the nanoscale. However, controlling the ordering and hence the properties is difficult due to a vast number of design parameters including the choice of monomer species, block sequence, block molecular weights and dispersity, polymer architecture, and binary interaction parameters. Navigating through this design space in order to find an optimal formulation for a target property requires an approach that efficiently searches through the countless parameter combinations in an automated fashion. We report on an inverse design strategy to target bulk morphologies utilizing particle swarm optimization (PSO) as a global optimizer and self consistent-field theory (SCFT) as a forward prediction engine. To avoid metastable states in the forward prediction, we utilize pseudospectral variable cell SCFT initiated from a library of defect-free seeds of known block copolymer morphologies. We demonstrate that our approach allows for a robust identification of block copolymers and copolymer alloys that self-assemble into a targeted structure, optimizing parameters such as block fractions, blend fractions, and Flory chi parameters.