文献类型：期刊文献

英文题名：Autonomous Construction of Phase Diagrams of Block Copolymers by Theory-Assisted Active Machine Learning

作者：Zhao, Shuochen[1];Cai, Tianyun[1];Zhang, Liangshun[1];Li, Weihua[2];Lin, Jiaping[1]

机构：[1]East China Univ Sci & Technol, Shanghai Key Lab Adv Polymer Mat, Key Lab Ultrafine Mat, Minist Educ,Sch Mat Sci & Engn, Shanghai 200237, Peoples R China;[2]Fudan Univ, Dept Macromol Sci, State Key Lab Mol Engn Polymers, Key Lab Computat Phys Sci, Shanghai 200438, Peoples R China

年份：2021

卷号：10

期号：5

起止页码：598

外文期刊名：ACS MACRO LETTERS

收录：;EI(收录号：20212210427346);WOS:【SCI-EXPANDED(收录号:WOS:000654292600014)】；

基金：This work was supported by the National Natural Science Foundation of China (22073028, 21873029, 51833003, and 21925301). We sincerely thank the anonymous reviewers for their helpful suggestions, which result in substantial improvements of the model.

语种：英文

外文关键词：Molecular orbitals - Nanostructured materials - Graphic methods - Machine learning - Mean field theory - Phase diagrams

摘要：Equilibrium phase diagrams serve as blueprints for rational design of nanostructured materials of block copolymers, but their construction is time-consuming and requires profound expertise. Herein, by virtue of the knowledge of self-consistent field theory (SCFT), the active-learning method is developed to autonomously construct the phase diagrams of block copolymers. Without human intervention, the SCFT-assisted active-learning method can rapidly search the undetected phases and efficiently reproduce the complicated phase diagrams of diblock copolymers and multiblock terpolymers via decreasing the number of sampling points to about 20%. It is clearly demonstrated that the combined uncertainty sampling/random selection scheme in the active-learning method shows the outperformance in spite of a small amount of initial data set. This work highlights the promising integration of theoretical modeling with machine learning and represents a crucial step toward rational design of nanostructured materials.