In this step, we developed ML models by implementing state-of-the-art algorithms to compare their performances on predicting reaction energies. We applied five methods including, ANN, RF, GCN, LGBM, and eXtreme Gradient Boosting (XGB). All the models are trained using ECFC encoder except GCN which contains its own integrated encoder. We trained and tuned the parameters of the models using the training set. The best-performing configurations for each method are tested on the reserved test sets. While all models showed excellent performance on Test-1, ANN and GCN models showed significantly better performance on Test-2. Among all the ML models, ANN yielded the top performance on both datasets. Finally, by combining the top-performing three models (ANN, RF, and GCN), we developed a weighted ensemble model (see Equation ( )). The ensemble model improved the performance for both test sets. The test results of singular models and the ensemble model are shown in Table given as MAE, RMSE, and R 2 . Figure