Inverse design of smart mechanical metamaterials based on liquid crystal elastomers

Liquid crystal elastomers (LCEs) are advanced materials characterized by their rubber-like hyperelasticity and liquid crystal phase transitions, offering exceptional mechanical properties. The development of smart mechanical metamaterials (SMMs) from LCEs expands the potential for controlling mechanical responses and achieving nonlinear behaviors not possible with traditional metamaterials. However, the challenge lies in managing the interplay between nonlinear material responses and structural complexity, making the inverse design of LCE-based SMMs exceptionally demanding. In this paper, we introduce a design framework for LCE smart mechanical metamaterials that leverages neural networks and evolution strategies (ES) to optimize designs with nonlinear mechanical responses. Our approach involves constructing a flexible, unit-cell-based metamaterial model that integrates the soft elastic behavior and thermo-mechanical coupling of LCEs. The combination of microscopic liquid crystal molecule rotation and macroscopic block rotation enables highly tunable and nonlinear mechanical behaviors, of which the precise inverse design of stress-stretch responses is obtained via neural networks combined with ES. In addition, stimuli responses in the liquid crystal elastomers enable real-time adaptability and achieve tailored stress plateaus that are not possible with traditional metamaterials. Our findings provide new pathways in the design and optimization of advanced materials in flexible electronic devices, intelligent actuators, and systems for energy absorption and dissipation.