A few examples of using LLMs in materials science have been recently presented. Jablonka et al. ( ) conducted a hackathon using LLMs such as GPT-4 for chemistry and materials science. The participants leveraged LLMs for a variety of purposes, such as predicting properties of molecules and materials, creating new tool interfaces, extracting knowledge from unstructured data, and developing educational applications. Being more specific, An et al. argued that the construction of knowledge graphs for domain-specific applications like metal-organic frameworks (MOFs) can be resource-intensive. LLMs, particularly domain-specific pre-trained models, have been successfully employed to create such graphs.