When utilizing Mordred descriptors as introduced by Notebook 6, correlation analysis can be performed to identify descriptors that significantly correlate with G ‡ . Here, Pearson correlation coefficients were computed between the Mordred descriptors of each substrate and catalyst and the respective G ‡ value. Descriptors with a correlation coefficient of 0.36 or higher were considered for the substrates, while descriptors with a correlation coefficient of 0.55 or higher were considered for the catalysts. The difference in the criteria was based on the number of descriptors correlating. Features meeting these criteria were further analyzed for significance using a two-tailed p-test over 5,000 random sample permutations using the Pearson correlation coefficient as the test statistic. Following feature generation, we applied one-hot encoding for categorical features and min-max scaling for continuous features. This statistical approach ensures that the selected descriptors are not only correlated with G ‡ , but are also statistically significant, minimizing the likelihood of spurious relationships. By standardizing and encoding the features, we ensure that our subsequent analyses are not biased by scale differences among the descriptors, facilitating more accurate and meaningful interpretations. LASSOCV and kNN exhibited moderate performance for the test set (R 2 ⇡ 0.70 and RMSE ⇡ 1.61 kJ/mol for both regressors, Table ), while other regressors underperformed, suggesting that Mordred descriptors may not be suitable for describing this type of reaction.