Data-driven inverse design of multifunctional bicontinuous multiscale structures.

Abstract

Bicontinuous multiscale structures, commonly observed in nature, comprise two interpenetrating networks that are solid and void phases forming a continuous and interconnected system. These unique architectures exhibit superior multi-physical performances and multifunctionalities; however, their design has been limited by the lack of analytical expressions and the computational challenges in multiscale optimization. This study presents a 3D Large-range, Boundary-identical, Bicontinuous, and Open-cell Microstructure (L-BOM) datasets for the fast data-driven inverse design of multifunctional bicontinuous multiscale structures. Each dataset features identical boundaries, bicontinuous open-cell structures, and broad property coverage for performance exploration. These properties are satisfied by active learning techniques developed with a generative artificial intelligence model. The datasets hold significant promise for advancing the design of bicontinuous multiscale structures with a large-range property space, without additional post-processing to ensure the connectivity. This work further demonstrates the potential of the datasets in devising bone implants, chair components, and multifunctional materials with tunable elasticity and permeability.