Automated Search Strategy for Novel Ordered Structures of Block Copolymers,ACS Macro Letters

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Automated Search Strategy for Novel Ordered Structures of Block Copolymers
ACS Macro Letters ( IF 5.1 ) Pub Date : 2024-07-23 , DOI: 10.1021/acsmacrolett.4c00384
Qingshu Dong ₁ , Zhanwen Xu ₁ , Qingliang Song ₁ , Yicheng Qiang ₁ , Yu Cao ₂ , Weihua Li ₁

Affiliation

Block copolymers with different architectures can possibly generate innumerable stable or metastable structures and thus provide an irreplaceable platform for theoretically exploring novel structures. Self-consistent field theory (SCFT) is a powerful tool to predict the ordered structures of block copolymers; however, it is sensitively dependent on its initial condition. Here we propose to use multiple symmetry-adapted basis functions to generate the initial conditions of SCFT and then apply Bayesian optimization to search for ordered structures by navigating the coefficient space of these basis functions. Without any prior knowledge, our scheme can automatically recover hundreds of ordered structures for two simple block copolymers, including most of the common structures and complex Frank–Kasper structures, together with many novel structures. By applying the automated scheme to various block copolymers, a huge number of novel structures can be obtained to expand the structural library, which may create new opportunities for the scientific community.

中文翻译：

嵌段共聚物新型有序结构的自动搜索策略

具有不同结构的嵌段共聚物可能产生无数的稳定或亚稳定结构，从而为理论上探索新型结构提供了不可替代的平台。自洽场论（SCFT）是预测嵌段共聚物有序结构的有力工具；然而，它敏感地依赖于其初始条件。在这里，我们建议使用多个对称自适应基函数来生成 SCFT 的初始条件，然后应用贝叶斯优化通过导航这些基函数的系数空间来搜索有序结构。在没有任何先验知识的情况下，我们的方案可以自动恢复两种简单嵌段共聚物的数百个有序结构，包括大多数常见结构和复杂的 Frank-Kasper 结构，以及许多新颖的结构。通过将自动化方案应用于各种嵌段共聚物，可以获得大量新颖的结构以扩展结构库，这可能为科学界创造新的机遇。

更新日期：2024-07-23

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