Self-assembly prediction of architecture-controlled bottlebrush copolymers in solution using graph convolutional networks.

The investigation of bottlebrush copolymer self-assembly in solution involves a comprehensive approach integrating simulation and experimental research, due to their unique physical characteristics. However, the intricate architecture of bottlebrush copolymers and the diverse solvent conditions introduce a wide range of parameter spaces. In this study, we investigated the solution self-assembly behavior of bottlebrush copolymers by combining dissipative particle dynamics (DPD) simulation results and machine learning (ML) including graph convolutional networks (GCNs).

Precise and selective macroscopic assembly of a dual lock-and-key structured hydrogel.

Macroscopic assembly offers immense potential for constructing complex systems due to the high design flexibility of the building blocks. In such assembly systems, hydrogels are promising candidates for building blocks due to their versatile chemical compositions and ease of property tuning. However, two major challenges must be addressed to facilitate application in a broader context: the precision of assembly and the quantity of orthogonally matching pairs must both be increased.