To evaluate the performance of our model, we employed several metrics: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Pearson correlation coefficient (P r), and R 2 score. RMSE and MAE provide insights into the magnitude of prediction errors, while P r assesses the strength of the linear relationship between observed and predicted values, and R 2 indicates the proportion of variance explained by the model. These metrics were used to evaluate our model's accuracy and robustness rigorously, and the results were compared with those from other studies to benchmark our model's performance against existing methods. The feature importance for each descriptor was calculated using the mean decrease of impurity.