Abstract:

A neural network approach was used to correct three parameters (a0, ax, and ac) in the B3LYP method for constructing a new B3LYP exchange correlation functional. A three-layer architecture which consisted of an input layer, a hidden layer, and an output layer, was adopted in the neural network. The total number of electrons, spin multiplicity, dipole moment, kinetic energy, quadrupole moment, and zero point energy were chosen as the most important physical descriptors. In this work, 296 energy values were randomly divided into two subsets: 246 energy values were used as the training set to determine the optimized structure of the neural network and the optimized synaptic weights; 50 energy values were used as a testing set to test the prediction capability of our neural network. Three modified parameters a0, ax, and ac that were obtained from the output layer were used to calculate thermochemical data such as the atomic energy (AE), ionization potential (IP), proton affinity (PA), total atomic energy (TAE), and standard heat of formation (△fHΘ). The newresults obtained, based on the neural network approach, are much better than the results calculated using the conventional B3LYP/6-311+G(3df,2p) method. Upon the neural network correction, the overall root-mean-square (RMS) error for the 296 species decreased from41.0 to 14.2 kJ·mol-1.

Key words: B3LYP functional, Neural network, Descriptor, Training set