Citation: WANG Xiu-Jun, LONG Mi. Statistical Correction of Heat of Formation Calculated by the O3LYP Method[J]. Acta Physico-Chimica Sinica doi: 10.3866/PKU.WHXB201207172 shu

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 Date: 9 April 2012
Available Online: 17 July 2012
Fund Project: 国家自然科学基金(20975040) (20975040)广东省自然科学基金(10351064101000000)资助项目 (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 (Δ_fH_calc^Θ) 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 Δ_fH_calc^Θ 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 Δ_fH_calc^Θ for a 40 individual testing set. The results demonstrated that the root mean square (RMS) deviations between the calculated and experimental Δ_fH^Θ 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.
- O3LYP,
- Neural network,
- Multiplelinear regression,
- Heat of formation,
- Root mean square deviation
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