Acta Phys. -Chim. Sin. ›› 2012, Vol. 28 ›› Issue (11): 2581-2588.doi: 10.3866/PKU.WHXB201207172


Statistical Correction of Heat of Formation Calculated by the O3LYP Method

WANG Xiu-Jun, LONG Mi   

  1. Key Laboratory of Fuel Cell Techology of Guangdong, College of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou 510640, P. R. China
  • Received:2012-04-09 Revised:2012-07-16 Published:2012-10-17
  • Supported by:

    The project was supported by the National Natural Science Foundation of China (20975040) and Natural Science Foundation of Guangdong Province, China (10351064101000000).


The results of density functional theory calculations are known to contain inherent numerical errors caused by various intrinsic approximations. In this paper, O3LYP/6-311+G(3df,2p)//O3LYP/6-31G(d) calculations were used to derive the heats of formation (ΔfHcalcΘ) of 220 small to medium-sized organic molecules, followed by the application of artificial neural network (ANN) and multiple linear regression (MLR) analyses to correct the values. The physical descriptors chosen were ΔfHcalcΘ and zero point energy as well as the total quantities of atoms, hydrogen atoms, 2-center bonds, 2-center antibonds, 1-center valence lone pairs and 1-center core pairs. The ANN and MLR systems were initially constructed using a 180 training set. The trained ANN and MLR systems were subsequently used to predict values of ΔfHcalcΘ for a 40 individual testing set. The results demonstrated that the root mean square (RMS) deviations between the calculated and experimental ΔfHΘ values in the training set were reduced from 24.7 to 11.8 and 13.0 kJ·mol-1 after ANN and MLR corrections, respectively. For the individual testing set, the deviations (RMSD) were reduced from 21.3 to 10.4 and 12.1 kJ·mol-1, respectively. Based on these results, it can be concluded that ANN exhibits superior fitting and predictive abilities compared with MLR.

Key words: O3LYP, Neural network, Multiplelinear regression, Heat of formation, Root mean square deviation