To address these problems, we propose using the large language model GPT-4, to choose explanatory variables. GPT-4 is a model that possesses vast knowledge, including science, and is capable of logical reasoning. As of the writing of this paper, only two months have passed sinc e its public release, and revolutionary results are being reported one after another by GPT-4 and a large language model (LLM). Examples include the selection of machine learning algorithms, predicting structure-property correlations, in-context learning, and controlling robotic arms. Our approach utilizes the scientific knowledge that GPT-4 possesses for the purpose of embedding domain knowledge in tasks predicting structure-property correlations.