A combined first-principles calculation and neural networks correction approach for evaluating Gibbs energy of formation

Despite of their successes, the results of first-principles quantum mechanical calculations contain inherent numerical errors that are caused by inadequate treatment of electron correlation, incompleteness of basis sets, relativistic effects or approximated exchange-correlation functionals. In this work, we develop a combined density-functional theory and neural-network correction (DFT-NEURON) approach to reduce drastically these errors, and apply the resulting approach to determine the standard Gibbs energy of formation DeltaG(0) at 298 K for small- and medium-sized organic molecules. The root mean square deviation of the calculated DeltaG(0) for 180 molecules is reduced from 22.3 kcal.mol(-1) to 3.0kcal.mol(-1) for B3LYP/6-311+G(d,p). We examine further the selection of physical descriptors for the neural network.