The third point of significance lies in the interpretability of the chosen parameters. All the selected variables possessed straightforward physical-chemical meanings and were accompanied by concise explanatory notes from GPT-4. The series of inferential processes carried out by GPT-4 seemed to agree with the decision-making of an experienced researcher well-versed in chemistry and informatics. This suggests that embedding domain knowledge could be delegated to an LLM. Furthermore, the fact that this process of descriptor selection was accomplished in significantly less time than a human (in a matter of seconds) is intriguing. A human researcher, requiring ample time for reading and interpreting the descriptors, would need tens of minutes or more. This finding hints at the potential for significant efficiency gains by integrating LLMs in similar scientific workflows.