Enhancing Specific Stiffness of Nano-Architected Materials Through Generative AI and Active Learning.

Architected materials derive their high performance from geometry as much as from intrinsic material properties, yet most optimization frameworks search within predefined topology families, limiting the discovery of genuinely new morphologies. Here, we investigate whether superior architectures exist beyond the reach of these parameterized families, and demonstrate that they can be systematically discovered. We present a framework that searches directly in voxelated structural space. Cubic-symmetric unit cells drawn from strut-based, TPMS-based, and hybrid families are encoded into a continuous latent representation, and surrogate-guided multi-objective search with an active learning loop repeatedly converts promising predictions into high-fidelity validated data. Across nine rounds, the mean minimum Hamming distance from the initial dataset increases from 442 to 641 and the Pareto hypervolume rises from 0.106 to 0.113, confirming systematic expansion of the validated design domain. Computationally, the discovered architectures achieve 11-20% higher relative Young's modulus than initial designs at matched density. Fabricated via two-photon polymerization and pyrolysis, the optimized carbon nano-architectures outperform density-matched counterparts by 18-23% in Young's modulus and 26-40% in strength, reaching specific stiffnesses of 17.2, 22.3, and 21.3 MPa m3 kg- 1. These results establish that direct learning in structural space enables discovering mechanically efficient nano-architectures beyond the initial parameterized families.