Optimizing Chiral Metamaterials for Non-Reciprocal and Asymmetric Elastic Properties Using Machine Learning

Chiral metamaterials can be designed to exhibit both non-reciprocal and asymmetric elastic properties by leveraging the contact mechanism between the ligament and rigid circles. Machine learning techniques, specifically Bayesian optimization, can efficiently explore the large design space to identify optimal chiral structures that maximize these unique mechanical behaviors.