A generalized exchange-correlation functional: the Neural-Networks approach

A Neural-Networks-based approach is proposed to construct a new type of exchange-correlation functional for density functional theory. It is applied to improve B3LYP functional by taking into account of high-order contributions to the exchange-correlation functional. The improved B3LYP functional is based on a neural network whose structure and synaptic weights are determined from 116 known experimental atomization energies, ionization potentials, proton affinities or total atomic energies which were used by Becke in his pioneer work on the hybrid functionals [J. Chem. Phys. ${bf 98}$, 5648 (1993)]. It leads to better agreement between the first-principles calculation results and these 116 experimental data. The new B3LYP functional is further tested by applying it to calculate the ionization potentials of 24 molecules of the G2 test set. The 6-311+G(3{it df},2{it p}) basis set is employed in the calculation, and the resulting root-mean-square error is reduced to 2.2 kcal$cdot$mol$^{-1}$ in comparison to 3.6 kcal$cdot$mol$^{-1}$ of conventional B3LYP/6-311+G(3{it df},2{it p}) calculation.

Combined first--principles calculation and neural--network correction approach as a powerful tool in computational physics and chemistry

Despite of their success, the results of first-principles quantum mechanical calculations contain inherent numerical errors caused by various approximations. We propose here a neural-network algorithm to greatly reduce these inherent errors. As a demonstration, this combined quantum mechanical calculation and neural-network correction approach is applied to the evaluation of standard heat of formation $DelH$ and standard Gibbs energy of formation $DelG$ for 180 organic molecules at 298 K. A dramatic reduction of numerical errors is clearly shown with systematic deviations being eliminated. For examples, the root--mean--square deviation of the calculated $DelH$ ($DelG$) for the 180 molecules is reduced from 21.4 (22.3) kcal$cdotp$mol$^{-1}$ to 3.1 (3.3) kcal$cdotp$mol$^{-1}$ for B3LYP/6-311+G({it d,p}) and from 12.0 (12.9) kcal$cdotp$mol$^{-1}$ to 3.3 (3.4) kcal$cdotp$mol$^{-1}$ for B3LYP/6-311+G(3{it df},2{it p}) before and after the neural-network correction.