文献类型：期刊文献

英文题名：Deep-Learning-Assisted Understanding of the Self-Assembly of Miktoarm Star Block Copolymers

作者：Cui, Congcong[1];Cao, Yuanyuan[2];Han, Lu[1]

机构：[1]Tongji Univ, Sch Chem Sci & Engn, Shanghai 200092, Peoples R China;[2]East China Univ Sci & Technol, Minist Educ, Sch Mat Sci & Engn, Lab Low Dimens Mat Chem,Key Lab Ultrafine Mat, Shanghai 200237, Peoples R China

年份：2025

卷号：19

期号：11

起止页码：11427

外文期刊名：ACS NANO

收录：;EI(收录号：20251218084932);WOS:【SCI-EXPANDED(收录号:WOS:001444283800001)】；

基金：This work was financially supported by the National Natural Science Foundation of China (Grant No. 22425303 to L.H., 22472058 to Y.C.) and the Fundamental Research Funds for the Central Universities (L.H., Y.C.). This work was carried out with the support of Shanghai Synchrotron Radiation Facility, instrument BL10U1.

语种：英文

外文关键词：deep learning; self-assembly; block copolymer; mesostructure; synthesis-field diagram

摘要：The self-assemblies of topological complex block copolymers, especially the AB n type miktoarm star ones, are fascinating topics in the soft matter field, which represent typical self-assembly behaviors analogous to those of biological membranes. However, their diverse topological asymmetries and versatile spontaneous curvatures result in rather complex phase separations that deviate significantly from the common mechanisms. Thus, numerous trial-and-error experiments with tremendous parameter space and intricate relationships are needed to study their assemblies. Herein, we applied deep learning technology to decipher the phase behaviors of the miktoarm star block copolymer PEO-s-PS2 in an evaporation-induced self-assembly system. A neural network model was trained from practical experimental data encompassing two polymer properties and three synthesis condition parameters as input variables, which successfully predicted a three-dimensional (3D) synthesis-field diagram and mined the relationship between input parameters and obtained structures. This model demonstrated the highly flexible structure modulation directions of the miktoarm star block copolymer, revealing the correlation between the polymer parameters, synthesis conditions, and the output structures due to the significant influence of the variables on spontaneous curvatures. This work demonstrated the efficiency of a deep learning technique in uncovering the underlying rules of complex self-assembly systems, providing valuable insights into the exploration of soft matter science.