Since GPT-4 only has general physical chemistry knowledge, mechanisms for learning through retrieving recent literature or fine-tuning are necessary. Building more advanced large language models could also automate machine learning tasks by suggesting descriptors to calculate from scratch or designing regression models. Alternatively, combining with models like symbolic regression that align well with theoretical research could provide interpretability and superior predictive performance. The linguistic selection process must also be revealed objectively by scientific approaches. By continuing to investigate these specific cases, we aim to uncover the extent to which large language models can serve as a potent tool in various chemical and material sciences.