The diffusion models generally operate in the following manner. During the noising process, noise is gradually introduced to a given data point, resulting in its perturbation. Conversely, in the denoising process, which is the reverse of the noising process, the model is trained to remove the introduced noise, allowing for the generation of new samples from random noises . In particular, among the various types of diffusion models, Denoising Diffusion Probabilistic Model (DDPM) has played an important role in the widespread adoption of diffusion models by greatly simplifying the loss term of the training process. In this work, on the top of original DDPM workflow, we developed a new diffusion model named zeolite diffusion (ZeoDiff) with the goal of generating pure silica zeolites.