Inverse design of soft materials via a deep learning-based evolutionary strategy

Area of Science:

• Materials Science
• Soft Matter Physics
• Computational Chemistry

Background:

• Colloidal self-assembly is crucial for advanced materials, but exploring diverse building blocks and conditions is challenging.
• Current methods struggle with systematic inverse design of colloidal structures.
• Developing a versatile algorithm for targeted self-assembly is a key research goal.

Purpose of the Study:

• To introduce a generic inverse design method for colloidal self-assembly.
• To enable efficient reverse-engineering of crystals, quasicrystals, and liquid crystals.
• To provide a computational approach for designing experimentally feasible colloidal interactions.

Main Methods:

• Utilizing an evolutionary strategy for parameter optimization.
• Employing a convolutional neural network as an order parameter.
• Targeting specific diffraction patterns to guide the inverse design process.

Main Results:

• Demonstrated an efficient inverse design method for colloidal self-assembly.
• Successfully reverse-engineered various ordered structures including crystals, quasicrystals, and liquid crystals.
• Developed a computational framework for designing colloidal interactions tailored to specific structures.

Conclusions:

• The developed algorithm offers a robust and versatile approach to inverse design in colloidal self-assembly.
• This method facilitates the creation of next-generation materials with precisely controlled structures.
• It provides a pathway for the experimental realization of designer colloidal systems.