AI-driven Automated Construction of Block Copolymer Phase Diagrams

Qing-Shu Dong; Qing-Liang Song; Kun Tian; Wei-Hua Li

doi:10.1007/s10118-025-3396-5

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AI-driven Automated Construction of Block Copolymer Phase Diagrams

RESEARCH ARTICLE | Updated：2025-09-26

- AI-driven Automated Construction of Block Copolymer Phase Diagrams
- AI-driven Automated Construction of Block Copolymer Phase Diagrams
- 高分子科学（英文） 2025年43卷第10期页码：1730-1738
- Affiliations：
  
  State Key Laboratory of Molecular Engineering of Polymers, Research Center of AI for Polymer Science, Key Laboratory of Computational Physical Sciences, Department of Macromolecular Science, Fudan University, Shanghai 200433, China
- Author bio：
  
  qsdong@fudan.edu.cn (Q.S.D.)
  weihuali@fudan.edu.cn (W.H.L.)
- Funds：
  
  This work was financially supported by the National Natural Science Foundation of China (Nos. 52394272, 22333002, 22203018 and 22303017).
- DOI：10.1007/s10118-025-3396-5
  中图分类号：
- 收稿：2025-05-27，
  
  修回：2025-06-07，
  
  录用：2025-06-10，
  
  网络首发：2025-09-02，
  
  纸质出版：2025-10-05
- Accepted：
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Dong, Q. S.; Song, Q. L.; Tian, K.; Li, W. H. AI-driven automated construction of block copolymer phase diagrams. Chinese J. Polym. Sci. 2025, 43, 1730–1738

Qing-Shu Dong, Qing-Liang Song, Kun Tian, et al. AI-driven Automated Construction of Block Copolymer Phase Diagrams[J]. Chinese Journal of Polymer Science, 2025, 43(10): 1730-1738.
Dong, Q. S.; Song, Q. L.; Tian, K.; Li, W. H. AI-driven automated construction of block copolymer phase diagrams. Chinese J. Polym. Sci. 2025, 43, 1730–1738 DOI： 10.1007/s10118-025-3396-5.

Qing-Shu Dong, Qing-Liang Song, Kun Tian, et al. AI-driven Automated Construction of Block Copolymer Phase Diagrams[J]. Chinese Journal of Polymer Science, 2025, 43(10): 1730-1738. DOI： 10.1007/s10118-025-3396-5.

摘要

Self-consistent field theory (SCFT) enables precise calculation of block copolymer phase diagrams

yet it critically depends on human expertise for selecting candidate structures. This work integrates artificial intelligence with SCFT to achieve automated search of self-assembled structures and autonomous construction of phase diagrams

thereby minimizing reliance on human prior knowledge.

Abstract

The self-assembly of block copolymers serves as an effective approach for fabricating various periodic ordered nanostructures. By employing self-consistent field theory (SCFT) to calculate the phase diagrams of block copolymers

one can accurately predict their self-assembly behaviors

thus providing guidance for the fabrication of various novel structures. However

SCFT is highly sensitive to initial conditions because it finds the free energy minima through an iterative process. Consequently

constructing phase diagrams using SCFT typically requires predefined candidate structures based on the experience of researchers. Such experience-dependent strategies often miss some structures and thus result in inaccurate phase diagrams. Recently

artificial intelligence (AI) techniques have demonstrated significant potential across diverse fields of science and technology. By leveraging AI methods

it is possible to reduce reliance on human experience

thereby constructing more robust and reliable phase diagrams. In this work

we demonstrate how to combine AI with SCFT to automatically search for self-assembled structures of block copolymers and construct phase diagrams. Our aim is to realize automatic construction of block copolymer phase diagrams while minimizing reliance on human prior knowledge.

关键词

Keywords

references

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