Shanghai Key Laboratory of Advanced Polymeric Materials, Key Laboratory for Ultrafine Materials of Ministry of Education, School of Materials Science and Engineering, East China University of Science and Technology, Shanghai 200237, China.
State Key Laboratory of Molecular Engineering of Polymers, Key Laboratory of Computational Physical Sciences, Department of Macromolecular Science, Fudan University, Shanghai 200438, China.
ACS Macro Lett. 2021 May 18;10(5):598-602. doi: 10.1021/acsmacrolett.1c00133. Epub 2021 Apr 27.
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.
平衡态相图是合理设计嵌段共聚物纳米结构材料的蓝图，但构建相图既耗时又需要深厚的专业知识。在此，我们利用自洽场理论（SCFT）的知识，开发了一种主动学习方法，可自主构建嵌段共聚物的相图。无需人工干预，该 SCFT 辅助的主动学习方法可通过将采样点数量减少到约 20%，快速搜索未检测到的相，并有效地再现二嵌段共聚物和多嵌段三共聚物的复杂相图。实验清楚地表明，主动学习方法中的组合不确定度采样/随机选择方案在初始数据集较少的情况下表现出色。这项工作突出了理论建模与机器学习的有前途的结合，代表了朝着合理设计纳米结构材料迈出的关键一步。
