Latent Diffusion Process With Mechanistic Guidance For Designing Functionally Graded Metamaterials With Perfect Connectivity

Recent advancements in artificial intelligence (AI)-based design strategies have expanded the ability to generate complex mechanical metamaterials across multiple length scales. However, achieving precise control of mechanical properties while preserving structural connectivity remains a major challenge, especially for functionally graded metamaterials with heterogeneous unit cell architectures. Here, a latent diffusion-based design framework is proposed for 3D graph metamaterials that enables stable generation and accurate inverse design in a discrete, topology-aware latent space. By integrating vector-quantized latent representations with a diffusion-based generative process and mechanistic guidance, the framework effectively explores complex design spaces while steering generated structures toward target elastic properties. The proposed approach enables the generation of graph metamaterials ranging from repetitive lattices to functionally graded architectures with smooth mechanical transitions and robust connectivity. These results demonstrate that latent diffusion with mechanistic guidance provides a scalable alternative to conventional interpolation-based or purely data-driven generative models for mechanistically optimized metamaterial design.