Prior to this work, ZeoGAN was developed in our group that used GAN to generate pure silica zeolites and exhibited the ability to generate samples with user-desired properties. To make fair comparisons between the two models, we deliberately aimed to employ the same data representation, training dataset and post-processing procedures as ZeoGAN. In Figure , the performance of ZeoDiff and ZeoGAN was compared in terms of the percentage of valid zeolite structures generated using the models. Taking the ratio between the perfect zeolite structures generated (after post-processing) to the total number of generated grids, ZeoDiff demonstrated over a 2,000-fold improvement compared to ZeoGAN (ZeoDiff : 1.83 %, ZeoGAN : 0.0008 %). This implies that ZeoDiff possess a superior capability to generate realistic samples when compared to ZeoGAN, highlighting that the advantage of the diffusion models seen in image processing extends also to material generation.