The potential of GPT in natural language processing (NLP), such as ChatGPT, has spurred interest in their applications beyond textual data, particularly in domains such as chemistry and materials science. The success of AtomGPT (Atomistic Generative Pretrained Transformer) , which demonstrated the capability to generate atomic structures and predict material properties using transformer-based architectures, highlights the power of transformer models in handling materials data. AtomGPT establishes the relationship between atomic configurations as text and material properties, allowing it to tackle both forward and inverse design problems.