Mechanism narrator. We anticipate LLMs will excel in applying established scientific principles to novel experimental phenomena. A significant portion of the work in the scientific mechanism exploring stage are pattern matching tasks (e.g. extracting subtle features from a spectrum and comparing with the standard database), which fall within the competence of LLMs. A typical procedure could be as straightforward as asking LLMs: a sample with this composition and processing history exhibited superior performance, here are all the characterization results (Scanning Electron Microscope, X-Ray Diffraction, etc.), please explain why this result is so good. Human researchers may examine the most reasonable explanations from a range of narratives LLMs generate, and start the scientific discussions from there. However, this process will pose the greatest challenge, with requirements including (i) image input and alignment with scientific terms, (ii) information retrieval capabilities from online physical scientific databases, (iii) LLMs being pretrained on scientific journal main context and supplements, (iv) cutting-edge subfield ML models invokable as plug-ins.