After calculating the possible reactions, we list the types and numbers of bonds in each molecule in the dataset. The coordinates were first converted to SMILES using the RDKit package as described by Kim et al. Four types of bonds, such as single, double, triple, and aromatic, were calculated for the molecules. As the molecules of the QM9 dataset only contains {C, H, N, O, and F}, these four type of bonds are sufficient. Then, we computed the list of bonds changed for all the 162,490,067 reactions. Only 20 specific types of bond were found to have changed in these reactions. The distribution of the change of each type of bond is given in Figure . For the estimation of total error arising from a DFT functional, we use the G4MP2 method as the ground truth. To demonstrate the effectiveness of the energy correction, we explore three DFT functionals in different rungs of Jacob's ladder, viz., PBE (GGA), B3LYP-D (hybrid), and M06-2X (meta-GGA hybrid) obtained by post-SCF calculation. The error in reaction energy is given by i = ∆E r,G4M P 2 -∆E r,i , where, i = { PBE, B3LYP-D, or M06-2X }. The linear regression was performed on all the 162,490,067 reactions and therefore, the entire dataset was used as the training set. We do not include an intercept in the linear regression because if there is no bond change for a chemical reaction, the error from DFT should be zero for an ideal functional with accuracy similar to G4MP2. The coefficients for each bond from the linear regression are added to ∆E i , where i = { PBE, B3LYP-D, and M06-2X }, to obtain the corrected reaction energy. To show the consistency of the coefficients obtained from linear regression, we performed a data sparsification by taking all the bonds with the correction coefficients ≥ 0.1 eV, obtained by analysis done with PBE energies. The sparsification is not particularly important in this dataset as the number of bonds to be corrected is small. However, if one needs to extend this method to include a vast variety of bonds (e.g. many different types of atoms involved) to maintain simplicity and robustness, sparsification might be necessary.