Swarm Intelligence Platform for Multiblock Polymer Inverse Formulation Design

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• 作者：Sean P. Paradiso ; Kris T. Delaney ; Glenn H. Fredrickson
• 刊名：ACS Macro Letters
• 出版年：2016
• 出版时间：August 16, 2016
• 年：2016
• 卷：5
• 期：8
• 页码：972-976
• 全文大小：306K
• 年卷期：0
• ISSN：2161-1653

文摘

Multiblock polymers (MBPs) show great promise as a platform for synthesizing materials with highly customized nanostructures. In these systems, the subtle balance of chain configurational entropy and interactions between dissimilar block chemistries emerge a rich palette of self-assembled structural motifs that may be leveraged to impart novel functional, mechanical, or optical properties to the final material. Unfortunately, extensive study of MBP self-assembly has yielded few reliable heuristics for intuitively navigating the enormous molecular design space made available by modern polymer synthesis techniques. In order to make progress, new methods must be developed that allow researchers to efficiently screen molecular designs for achieving a desired state of self-assembly. Here, we introduce a platform for the automated discovery of tailored MBP formulations based on the Particle Swarm Optimization (PSO) method and a linear multiblock chain parametrization that enables continuous optimization of chain architecture. We apply the method to thin-film blends of linear ABC triblock polymers subject to lateral confinement, allowing all polymer and blend parameters to freely optimize in search of a prespecified, nontrivial target morphology. While we focus on pattern selection as a proof of principle, any computable equilibrium property can be optimized using the methods described here.