The refined datasets were utilized to develop auxiliary predictive models. The melting point dataset was compiled from two sources: one containing molecular compounds 38 with 47,427 substances, and another covering salts 39 with 3,064 substances. Data for critical temperature (T c ), critical pressure (P c ), and enthalpy of vaporization (∆H v ) were obtained from Yaws' publication, Distributed hyperparameter optimization for GCNN models was conducted using Ray Tune. For CatBoost models, the default hyperparameters were employed.