Simple arithmetic operation in latent space can generate a novel three dimensional graph metamaterials

Abstract

Recent advancements in artificial intelligence (AI)-based design strategies for metamaterials have revolutionized the creation of customizable architectures spanning nano- to macro-scale dimensions, achieving unprecedented mechanical behaviors that surpass the inherent properties of the constituent materials. However, the growing complexity of these methods poses challenges in generating diverse metamaterials without substantial human and computational resources, hindering widespread adoption. Addressing this, our study introduces an innovative design strategy capable of generating various three-dimensional graph metamaterials using simple arithmetic operations within the latent space. By seamlessly integrating hidden representations of disentangled latent space and latent diffusion processes, our approach provides a comprehensive understanding of complex design spaces, generating diverse graph metamaterials through arithmetic operations. This methodology stands as a versatile tool for creating structures ranging from repetitive lattice structures to functionally graded mechanical metamaterials. It also serves as an inverse design strategy for diverse lattice structures, including crystalline structures and those made of trabecular bone. We believe that this methodology represents a foundational step in advancing our comprehension of the intricate latent design space, offering the potential to establish a unified model for various traditional generative models in the realm of mechanical metamaterials.