  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.
