There is a growing belief that LLMs encapsulate the complexities of the real world within their parameters, enabling them to perform a myriad of tasks with unprecedented efficacy. In the field of materials science, researchers have tested its performance on the composition tasks of Matbench. The results indicate that, while GPTChem 3 surpasses the baseline dummy model, it falls short of the performance achieved by human-designed models. This suggests that although it can identify certain laws and relationships in materials, its understanding remains inferior to that of humans. Recent studies on generative stable materials have demonstrated that LLMs can be effectively utilized in material design. One such study introduces Chemeleon, a generative model utilizing a BERT-based variant (Crystal CLIP) with contrastive learning to align textual descriptions with 3D crystal structures. By employing diffusion models, it efficiently generates stable materials in complex chemical spaces like Li-P-S-Cl, showcasing the potential of generative AI in materials design. Another study developed by , employed GPT-2 to generate stable materials and made their findings accessible through a public website (). Additionally, Meta has explored the use of open-source LLM, LLaMA2, to identify stable materials. Their findings indicated a performance that was twice as effective as that of the Crystal Diffusion Variational Autoencoder (CDVAE). Throughout the history of materials science, numerous methods have been developed to evaluate materials by testing various properties, with a primary focus on assessing their stability and suitability for specific applications. Consequently, there is a strong correlation between a material's properties and its structural features. Based on our experience, a model's ability to accurately generate structures that meet specific targets once does not necessarily indicate a deep understanding of the related properties. To substantiate such an understanding, the model must also demonstrate high precision in predicting these properties. Otherwise, its success in generation could simply be attributed to chance.