Distinguish microphase-separated structures of diblock copolymers using local order parameters

Area of Science:

• Polymer Science
• Materials Science
• Computational Chemistry

Background:

• Block copolymers exhibit complex microphase-separated structures.
• Characterizing these ordered structures is crucial for understanding their properties.
• Existing methods may lack precision in distinguishing adjacent morphologies.

Purpose of the Study:

• To develop and validate new local order parameters (LOPs) for classifying block copolymer microphase-separated structures.
• To systematically evaluate a large set of candidate LOPs using computational tools.
• To assess the robustness and applicability of the selected LOPs across the phase diagram.

Main Methods:

• Utilized the Molecular Assembly structure Learning package for Identifying Order parameters (MALIO) for LOP evaluation.
• Generated a dataset of LOP values for various microphase-separated structures.
• Employed supervised machine learning to select and refine the best LOPs.
• Assessed LOP classification accuracy against variations in miscibility and block fraction.

Main Results:

• Identified a set of effective LOPs that accurately distinguish between adjacent microphase-separated structures.
• Demonstrated that the minimum local area for LOP performance relates to structure size.
• Showcased the robustness of selected LOPs under varying copolymer compositions.
• MALIO facilitated rapid and systematic selection of optimal LOPs.

Conclusions:

• The proposed LOPs offer a precise method for classifying block copolymer morphologies.
• These LOPs are applicable over a wide range of the block copolymer phase diagram.
• The MALIO package provides an efficient platform for discovering structure-property descriptors.