Open Quantum Materials Database (OQMD) , and Computational Materials Repository Castelli-perovskites database (CMR) (see more details in Text S1 in Supporting Information). For data extraction, we employed two data mining tools, namely Open Databases Integration for Materials Design (OPTIMADE) and Matminer . Figure shows the workflow of screening perovskite structures from these three databases and the number of structures selected after each step. Initially, we queried inorganic materials with stoichiometric ABX 3 compositions, yielding 4,632 compounds from the MP dataset and 28,026 from the OQMD dataset. Then, we selected perovskite structures with precisely 5 atoms per unit cell, excluding complex forms like A 2 BB'X 6 double perovskite, to ensure consistency in feature construction. This step narrowed down the MP dataset to 1,784 structures and the OQMD dataset to 13,090 structures. In the third step, we further impose a filter condition to identify perovskite crystal structures, which inherently contain octahedral motifs formed by A or B with X. This filter, utilizing the Voronoi algorithm , resulted in 1,559 structures from MP and 11,349 structures from OQMD. Consequently, certain ABX 3 crystals that do not exhibit the perovskite structure were excluded (see example case in Figure ). Finally, we identified and excluded any exceptions from the previous step using the StructureMatcher function in Pymatgen along with 13 perovskite prototype structures from Gu's work . Thus, we collected a total of 1, 517 structures from the MP and 11, 265 structures from the OQMD. For the CMR dataset, which exclusively comprises perovskite materials, we gathered all 18,928 data points avail-able without modification. E f and E g , which are shared properties across these three databases, were the focus of our ML prediction efforts.