Using knowledge graphs also removes the hallucination effect, an error made by LLMs when rendering combinations that appear plausible to the model's probability measures but false when tested against high-fidelity information or logic. It appears due to multiple factors, such as when LLMs are trained on contradictory datasets, overfitting, etc. An urgent and vital topic in LLMs is, therefore, quantifying the level of the hallucination effects and developing a systematic method to recognise and mitigate them. On the other hand, LLMs have the advantage that they can process and understand the context from scientific literature, patents, and database entries. When combined with knowledge graphs that structure this information, it provides a rich database of materials science knowledge which can be readily queried. This integration allows for the rapid assimilation of existing knowledge and the identification of knowledge gaps.