where ] _ are fitted weighing coefficients and `_ are the selected analytical expressions, which correspond to components of a descriptor vector. Because the functional forms of SISSO models are flexible and can easily fit the rather small number of materials considered in this work, the appropriate model complexity needs to be determined. This is done to avoid identifying models that fit the training data but which are not generalizable. We evaluated the optimal complexity with respect to the predictability through a leave-one-material-out-cross-validation (CV) approach. Details of the SISSO analysis are provided in the ESI. The 20 parameters obtained from all the characterization techniques were used as primary features in the SISSO analysis. Additionally, we included the elemental parameters electronegativity and ionization energy that reflect the chemical nature of the A and B elements of the perovskite, RS ( , RS , , TR ( and TR , . These physicochemical parameters are associated to free atoms. They were measured are are reported in the literature (see values in Table ). In total, 24 primary features were considered in our SISSO analysis. The full list of primary features is shown in Table of the ESI. The values of these primary features for all the materials discussed in this paper are available in the dataset.xls supplementary file.