文献类型：期刊文献

英文题名：Inverse Design of Complex Block Copolymers for Exotic Self- Assembled Structures Based on Bayesian Optimization

作者：Dong, Qingshu[1];Gong, Xiangrui[2];Yuan, Kangrui[1];Jiang, Ying[2];Zhang, Liangshun[3];Li, Weihua[1]

机构：[1]Fudan Univ, Dept Macromol Sci, State Key Lab Mol Engn Polymers, Key Lab Computat Phys Sci, Shanghai 200433, Peoples R China;[2]Beihang Univ, Ctr Soft Matter Phys & its Applicat, Sch Chem, Beijing 100191, Peoples R China;[3]East China Univ Sci & Technol, Sch Mat Sci & Engn, Shanghai Key Lab Adv Polymer Mat, Shanghai 200237, Peoples R China

年份：2023

卷号：12

期号：3

起止页码：401

外文期刊名：ACS MACRO LETTERS

收录：;EI(收录号：20231113729226);WOS:【SCI-EXPANDED(收录号:WOS:000947255300001)】；

基金：? ACKNOWLEDGMENTS This work was supported by the National Natural Science Foundation of China (Grants 21925301, 22203018, 22073004, and 22073028) and the Fundamental Research Funds for the Central Universities.

语种：英文

外文关键词：Fast Fourier transforms - Mean field theory - Molecular orbitals - Structural optimization

摘要：Variable chain topologies of multiblock copolymers provide great opportunities for the formation of numerous self-assembled nanostructures with promising potential applications. However, the consequent large parameter space poses new challenges for searching the stable parameter region of desired novel structures. In this Letter, by combining Bayesian optimization (BO), fast Fourier transform-assisted 3D convolutional neural network (FFT-3DCNN), and self-consistent field theory (SCFT), we develop a data-driven and fully automated inverse design framework to search for the desired novel structures self-assembled by ABC type multiblock copolymers. Stable phase regions of three exotic target structures are efficiently identified in high-dimensional parameter space. Our work advances the new research paradigm of inverse design in the field of block copolymers.