In the portfolio of model-based artificial intelligence (AI) methods, LLMs seem to offer new opportunities to discover materials and processes that may otherwise remain hidden in the complexity and scattered information that already exists . One avenue to use LLMs is accelerated materials discovery . This is due to the fact that language-based token systems that connect words based on probability are particularly strong in extracting and combining knowledge that already exists in text form. Therefore, while LLMs may not be necessarily suited for disruptive conceptual discoveries from text connections, they can accelerate design based on existing concepts . Although this is a rather conservative approach, it is already a big step forward, because the traditional trial-and-error approach of material discovery is time-consuming and resource-intensive. Also, LLMs can analyse vast datasets, extracting patterns and correlations that would elude human researchers. For instance, LLMs can process published literature, patents, and experimental data to suggest combinations of novel material compositions and even possible properties, as will be shown below in more detail. By integrating databases like the Materials Project or the Cambridge Structural Database, LLMs can offer quantitative predictions about material structures, compositions, and potential applications, significantly reducing the time from conception to application.