Dierk Raabe 1 , Zongrui Pei 2 , Junqi Yin 3 , James Saal 4 , Kasturi Narasimha Sasidhar 5 , Jörg Neugebauer Such databases can then be used by other machine learning (ML) methods. With their direct role in materials discovery we mean that LLMs can even extract causal relationships from collected data, serve to build domain-specific knowledge graphs, render hypotheses and guide progress-critical experiments, data collection and simulation (1-3). The latter aspect is essential because LLMs do not obey any built-in causal rules. Instead, they connect language tokens in a probabilistic way, without considering logic, selfconsistency or conservation laws. This means that they can violate elementary scientific rules. They mimic scientific context by using probability measures that rest on majority but not on proof or logic. This explains why there are opportunities but also pitfalls. The latter can be mitigated by combining LLMs with other methods such as classical theory, thermodynamics, kinetics, materials property data bases, explainable artificial intelligence, active learning etc. LLMs are also capable of generating hypotheses and they can be used to build domain-specific knowledge graphs which in turn can enhance predictive models .