Since the pretraining of language models typically involves large amounts of data, it is crucial to ensure that no data leakage occurs with the matbench dataset. The results of this verification are presented in Fig. . All material property predictions are at the level of a Dummy model, regardless of whether the input is raw (e.g., CIF files for structure tasks or chemical formulas for composition tasks in matbench) or provided as natural language descriptions. Among all tasks, the results for four tasks ( The values beside the "Dummy" and "Best" lines represent the reported metrics for each task. "CIF" and "RAW" refer to the original formats used in matbench for structure and composition tasks, respectively, while "NL" represents the transformed natural language format generated by us.