Causal Language Models such as GPT, GPT-2, and GPT-3 are trained to calculate/predict the probability of the occurrence of several words given all preceding words, making them ideal for the text generation - . After characterlevel RNNs and masked transformer language model had been used to capture Simplified Molecular Input Line Entry System (SMILES) language patterns , , Sanjar Adilov constructed a GPT-2-like language model to learn SMILES representations and transfer knowledge to downstream molecular generative tasks . In subsequent studies, GPT has attracted the attention of more researchers , . Although research on the application of GPT to the design of stimulusresponsive materials is still at an early stage, there is no doubt that such an attempt would be of interest.