This approach highlights the potential of fine-tuned LLMs in materials informatics and provides a foundation for further investigation. Future work could involve developing more generalized models and refining training techniques to better integrate diverse tasks. Notably, due to their natural language understanding, LLMs are adept at handling varied queries and interpreting semantic relationships, making them well-suited for providing coherent outputs. This capability allows materials science researchers to interact with the model using natural language and obtain meaningful outputs based on their queries, streamlining the process of material discovery and design.