In this study, the material variable was converted into numerical features using Xenonpy 10 and converted into a format that could be processed by machine learning algorithms. The procedure is described in the following section. First, chemical compositions were converted into comp_dict, a dictionary of proportions by element, using Pymatgen 6,7 . Subsequently, 290 compositional features based on comp_dict were calculated from 58 features of each element (atomic number, bond radius, van der Waals radius, electronegativity, thermal conductivity, bandgap, polarizability, boiling point, and melting point, etc.) . All chemical compositions used in this study were normalized. Features with zero variance and duplicate features were excluded.