LLMs offer great potential in the complex interplay between advanced computational methods and the nuanced, often experimentally and empirically grounded field of materials science. Opportunities lie in accelerated material discovery; enhancement and improved pattern and result analysis of data obtained from existing computational tools such as atomistic simulations; better knowledge synthesis and data management from research articles, reports, and property studies; support in hypothesis development and outlier analysis; and advanced decision-making support in materials selection and design, including aspects such as costs, sustainability and regulatory constraints. Pitfalls exist regarding the quality, availability, bias and legal status of the training data; lack of built-in logic or conservation laws; lack of the reflection of microstructure, synthesis, sustainability and processing complexity; and the danger of overreliance and even complacency regarding LLM predictions, i.e. the decay of individuals' own critical thinking, rigorous validation or falsification and the thrive towards deep understanding of the underlying causality behind phenomena which are key factors that have made the scientific method the most successful and reliable approach in history.