To investigate potential noise in the feature space, we gathered key features to reduce dimensionality, resulting in a new dataset comprising six-key-features (6KFs) descriptors. Consequently, the 6KFs space encompasses 24 features. Performance metrics indicate that the predictability of the final model remained almost unchanged and performed similarly to other models. This finding encouraged us to establish a systematic protocol for detecting noise in the dataset, specifically targeting outliers, i.e., measurements that are incorrect or scarcely represented in the dataset. Table . Detailed information on the RFR models and descriptors: test set RMSE, MAE, and R 2 were calculated for the model performance assessment. N est and f max denote the number of decision trees that will be running in a model and the maximum number of features a model considers when determining a split, respectively. f max defines the ratio of features to consider when looking for the best split while training each tree in the forest. NOF is the number of features in each descriptor space.