Artificial neural network study for the estimation of the C–H bond dissociation enthalpies

The three-layered feed-forward type artificial neural network (ANN) was applied to estimate the bond dissociation enthalpies (BDEs) of the C–H bonds in partially halogenated alkanes and ethers. The training values consisted of BDEs of the 93 C–H bonds in 53 alkanes and 24 ether molecules, which were computed by using density functional theory at the (RO)B3LYP/6-311G∗∗ level. As input parameters of ANN, we defined 14 kinds of topological descriptors, such as H–C⋯X (X=H, F, Cl, Br, O), H–C–C⋯X (X=H, F, Cl, O), H–C–C–C⋯X (X=H, F, O), H–C–O–C⋯X (X=H, F). Consequently, the average of absolute deviation in correlation and estimation were 0.70 and 0.84 kcal/mol, respectively. It was possible to construct a reliable and useful BDE estimation method.