Machine learning-based mechanics of TPMS architected materials driven by unit-cell geometric features

Architected materials have transformed engineering practice, enabling unprecedented control over effective thermal and mechanical properties. While inner design acts as a lever for the effective performance, deriving structure-property relations remains a major challenge, with an ever-increasing need for physics-motivated, wide-applicability models. This contribution elaborates a machine learning framework for the prediction of the effective mechanical and thermal properties directly from the unit-cell geometry. The formulation identifies and leverages a set of fundamental geometric features to accurately predict the Young’s and Shear modulus, Yield strength, and Thermal conductivity of architected TPMS materials based solely on their unit-cell’s Computer-Aided-Design (CAD) architecture. The resulting hypermodel provides high-accuracy predictions and the foundation for inner design explainability analysis, facilitating the identification of the most influential features. Topological descriptors that favour shear over uniaxial stiffness or yield strength are identified. Notably, a minimal set of three features, providing compactness, inner surface area, and shape variance information suffices to predict the effective TPMS mechanics within a 5% accuracy threshold. The results benchmark novel, physics-motivated, and interpretable modelling pathways, guiding material design and discovery.