The X1 method for accurate and efficient prediction of heats of formation

Abstract

We propose the X1 method which combines the density functional theory method with a neural network (NN) correction for an accurate yet efficient prediction of heats of formation. It calculates the final energy by using B3LYP6-311+G(3df,2p) at the B3LYP6-311+G(d,p) optimized geometry to obtain the B3LYP standard heats of formation at 298 K with the unscaled zero-point energy and thermal corrections at the latter basis set. The NN parameters cover 15 elements of H, Li, Be, B, C, N, O, F, Na, Mg, Al, Si, P, S, and Cl. The performance of X1 is close to the Gn theories, giving a mean absolute deviation of 1.43 kcalmol for the G399 set of 223 molecules up to 10 nonhydrogen atoms and 1.48 kcal/mol for the X107 set of 393 molecules up to 32 nonhydrogen atoms.