Vice versa, LLMs, with their ability to process and generate large volumes of text, can also serve to construct domain-specific knowledge graphs, optimize algorithms for faster discovery, and enable more efficient design and exploration of materials. The synergy between LLMs and knowledge graphs could hence be a useful next step to materials discovery, offering a paradigm shift from traditional, iterative experimental methods to a more quality-controlled data-driven model. This combination would allow better alignment of reliable high-quality data exploitation (through knowledge graphs) and semantic contextualization (through LLMs).