The data used in this study was collected from RedDB 9 version 1.0, which is a recently published computational database focused on the candidate electroactive compounds for AORFBs. In addition to calculated atomic and molecular data, RedDB contains 15,932 reaction pairs and their calculated reaction energies (see Equation (1)) that are used as the target property in the current study. From the published database, we removed the instances that contain missing reaction energy values and ended up with 15,794 reaction pairs. The chemical space of the data visualized by ChemPlot where each dot corresponds to a single molecule in 2-dimensions is shown in Figure . The molecules were placed based on their structural similarities extracted from their SMILES notations and the dimensions were reduced by the UMAP algorithm. Based on the chemical space occupation, we divided the data into three disjunct sets: Training, Test-1, and Test-2. In order to evaluate the performance distinctively on the training-and extrapolated-domain, we selected Test-1 instances from within the chemical space of the training set, while the Test-2 instances reside outside the chemistry space covered by the training set. The training set was used to train and tune the models while the test sets were reserved for the evaluation of ML model performance. As a result, 12,707 (80.5%) instances were reserved for Training, 1,607 (10.2%) for Test-1, and 1,480 (9.3%) for Test-2.