Automated Search Strategy for Novel Ordered Structures of Block Copolymers

Area of Science:

• Polymer Science
• Materials Science
• Computational Chemistry

Background:

• Block copolymers offer a versatile platform for generating diverse ordered structures.
• Self-consistent field theory (SCFT) is crucial for predicting these structures but is sensitive to initial conditions.

Purpose of the Study:

• To develop an automated, initial-condition-independent method for discovering block copolymer structures.
• To explore the vast structural landscape of block copolymers and identify novel arrangements.

Main Methods:

• Utilizing multiple symmetry-adapted basis functions to generate initial conditions for SCFT.
• Employing Bayesian optimization to navigate the coefficient space for structure prediction.
• Applying the automated scheme to various block copolymer systems.

Main Results:

• Successfully recovered hundreds of ordered structures for simple block copolymers, including known and novel Frank-Kasper structures.
• Demonstrated the ability to automatically identify a wide range of complex and previously unknown structures.
• Generated a substantial library of novel block copolymer structures.

Conclusions:

• The proposed automated scheme effectively overcomes SCFT's initial condition dependency.
• This method significantly expands the known structural diversity of block copolymers.
• The findings offer new opportunities for materials science and polymer research.