Inverse design of porous materials: a diffusion model approach

Abstract

The success of diffusion models in the field of image processing has propelled the creation of software such as Dall-E, Midjourney and Stable Diffusion, which are tools used for text-to-image generation. Mapping this workflow onto material discovery, a new diffusion model was developed for the generation of pure silica zeolite, marking one of the first applications of diffusion models to porous materials. Our model demonstrates the ability to generate novel crystalline porous materials that are not present in the training dataset, while exhibiting exceptional performance in inverse design tasks targeted on various chemical properties including the void fraction, Henry coefficient and heat of adsorption. Comparing our model with a Generative Adversarial Network (GAN) revealed that the diffusion model outperforms the GAN in terms of structure validity, exhibiting an over 2000-fold improvement in performance. We firmly believe that diffusion models (along with other deep generative models) hold immense potential in revolutionizing the design of new materials, and anticipate the wide extension of our model to other classes of porous materials.