We propose the X1 method which combines the density functional theory (DFT) method with a neural network (NN) correction for an accurate yet efficient prediction of heats of formation. It calculates the final energy by using B3LYP/6-311+G(3df,2p) at the B3LYP/6-311+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 kcal/mol for the G3/99 set of 223 molecules up to 10 nonhydrogen atoms, and 1.48 kcal/mol for the X1/07 set of 393 molecules up to 32 nonhydrogen atoms.

The X1 method offers an eXpress way to calculate accurate heats of formation of larger molecules, inaccessible by accurate wavefunction–based methods such as G2 or G3. The X1 method greatly eXtends the reliability and applicability of the B3LYP method, providing a valuable complement to the experimental determination of thermochemistry.