If the values of a particular variable for the experimental conditions were not present, it was assumed that these values were missing. An example of this case is shown in Figure . Subsequently, a two-stage machine-learning model was built using the dataset containing these missing values, which are written as NaN in Figure . In addition, Uniform Manifold Approximation and Projection (UMAP) , the dimensionality compression algorithm used in the present study, does not allow for the presence of missing values. The missing values were complemented as much as possible without compromising their intrinsic physical meaning. The yellow highlight in Figure (b) shows an example of missing value imputation. The missing values were complemented by replacing the calcination time with 0 h at room temperature (300 K). The missing values in the entire dataset were imputed as follows: processing time, 0 h; processing temperature, 300 K (room temperature); and number of elements in the synthesis, 0.