While the opportunities are vast, applying LLMs in materials science also has challenges. Data quality and availability are critical as models are only as good as the data they train on. Ensuring data integrity and representativeness is paramount. Furthermore, the interpretability of LLM outputs is crucial for gaining trust in their predictions. Developing models that can provide not just predictions but also insights into the underlying mechanisms is an essential goal. Another point is the Chain of Thought Prompting, an approach to enhance LLMs' comprehension of causal relationships and reduce hallucination. It involves forcing the models to verbalize different steps of reasoning they have gone through in reaching conclusions. This makes the process more transparent. Such ideas have not been implemented in materials science but in other areas such as medical science .