DiffMat: Data-driven inverse design of energy-absorbing metamaterials using diffusion model

Abstract

Energy-absorbing materials and structures are widely applied in industrial areas. Presently, design methods of energy-absorbing metamaterials mainly rely on empirical or bio-inspired configurations. Inspired by AI-generated content, this paper proposes a novel inverse design framework for energy-absorbing metamaterial using diffusion model called DiffMat, which can be customized to generate microstructures given desired stress–strain curves. DiffMat learns the conditional distribution of microstructure given mechanical properties and can realize the one-to-many mapping from properties to geometries. Numerical simulations and experimental validations demonstrate the capability of DiffMat to generate a diverse array of microstructures based on given mechanical properties. This indicates the validity and high accuracy of DiffMat in generating metamaterials that meet the desired mechanical properties. The successful demonstration of the proposed inverse design framework highlights its potential to revolutionize the development of energy-absorbing metamaterials and underscores the broader impact of integrating AI-inspired methodologies into metamaterial design and engineering.