CMS Trailblazer Symposium - Qianying Cao

Engineering design lies at the core of innovation across critical sectors. However, traditional design pipelines struggle with speed, scalability, and reliance on expert intuition. Machine learning is emerging as a transformative tool for the design of mechanical metamaterials, offering properties that far surpass those achievable through lab-based trial-and-error methods. Here, we introduce an end-to-end scientific ML framework, leveraging deep neural operators, to directly learn the relationship between the complete microstructure and mechanical response from sparse but high-quality in situ experimental data. The approach facilitates the efficient inverse design of structures tailored to specific nonlinear mechanical behaviors. Results obtained from stochastic spinodal microstructures, printed using two-photon lithography, reveal that the prediction accuracy for mechanical responses is very high. Our work marks a significant advancement in the field of materials-by-design, potentially heralding a new era in the discovery and development of next- generation metamaterials with unparalleled mechanical characteristics derived directly from experimental insights.