Inverse Design of Composite Materials Based on Latent Space and Bayesian Optimization

Inverse design of advanced materials represents a pivotal challenge in materials science. Leveraging the latent space of Variational Autoencoders (VAEs) for material optimization has emerged as a significant advancement in the field of material inverse design. However, VAEs are inherently prone to generating blurred images, posing challenges for precise inverse design and microstructure manufacturing. While increasing the dimensionality of the VAE latent space can mitigate reconstruction blurriness to some extent, it simultaneously imposes a substantial burden on target optimization due to an excessively high search space. To address these limitations, this study adopts a Variational Autoencoder guided Conditional Diffusion Generative Model (VAE-CDGM) framework integrated with Bayesian optimization to achieve the inverse design of composite materials with targeted mechanical properties. The VAE-CDGM model synergizes the strengths of VAEs and Denoising Diffusion Probabilistic Models (DDPM), enabling the generation of high-quality, sharp images while preserving a manipulable latent space. To accommodate varying dimensional requirements of the latent space, two optimization strategies are proposed. When the latent space dimensionality is excessively high, SHapley Additive exPlanations (SHAP) sensitivity analysis is employed to identify critical latent features for optimization within a reduced subspace. Conversely, direct optimization is performed in the low-dimensional latent space of VAE-CDGM when dimensionality is modest. The results demonstrate that both strategies accurately achieve the targeted design of composite materials while circumventing the blurred reconstruction flaws of VAEs, which offers a novel pathway for the precise design of advanced materials.