Feature-driven machine learning for prediction and inverse design of bistable twist metastructures with highly nonlinear responses

Mechanical metastructures offer promising applications in energy-absorbing devices and soft actuators. However, their highly nonlinear responses, especially sudden force drop within a quite narrow displacement range, pose a significant challenge in accurate prediction and inverse optimization. This study introduces a feature-driven machine learning approach that significantly enhances the predictive accuracy and efficiency of highly nonlinear responses. Herein, bistable twist metastructures with highly nonlinear sudden force drop responses are utilized as the platform. By employing feature-driven classification and reconstruction, force-displacement curves are modularized according to the feature types. The feature-driven sampling strategy increases the sampling density in highly nonlinear modules while reducing the redundant points in approximately linear modules. This facilitates the optimized distribution of sampling points in each module, ensuring the integrity of highly nonlinear features. Moreover, the feature-driven machine learning approach reduces the severe reliance on large data amount. Utilizing only 1500 data groups, a mapping paradigm between 9 geometric parameters and highly nonlinear responses is established through machine learning within only 1∼3 min, achieving a significantly higher accuracy. Additionally, inverse design is accomplished through the integration of trained network and search algorithms. Targeted on the pronounced structural hysteresis, which is advantageous to cushioning performance, desired mechanical responses are identified, exhibiting an enhancement exceeding 30%, with the corresponding geometric parameters effectively determined. It is believed that this feature-driven modular approach holds immense potentials for both precise prediction and effective optimization of mechanical metastructures with highly nonlinear responses.