Improving the accuracy of low level quantum chemical calculation for absorption energies: the genetic algorithm and neural network approach

The combination of genetic algorithm and back-propagation neural network correction approaches (GABP) has successfully improved the calculation accuracy of absorption energies. In this paper, the absorption energies of 160 organic molecules are corrected to test this method. Firstly, the GABP1 is introduced to determine the quantitative relationship between the experimental results and calculations obtained by using quantum chemical methods. After GABP1 correction, the root-mean-square (RMS) deviations of the calculated absorption energies reduce from 0.32, 0.95 and 0.46 eV to 0.14, 0.19 and 0.18 eV for B3LYP/6-31G(d), B3LYP/STO-3G and ZINDO methods, respectively. The corrected results of B3LYP/6-31G(d)-GABP1 are in good agreement with experimental results. Then, the GABP2 is introduced to determine the quantitative relationship between the results of B3LYP/6-31G(d)-GABP1 method and calculations of the low accuracy methods (B3LYP/STO-3G and ZINDO). After GABP2 correction, the RMS deviations of the calculated absorption energies reduce to 0.20 and 0.19 eV for B3LYP/STO-3G and ZINDO methods, respectively. The results show that the RMS deviations after GABP1 and GABP2 correction are similar for B3LYP/STO-3G and ZINDO methods. Thus, the B3LYP/6-31G(d)-GABP1 is a better method to predict absorption energies and can be used as the approximation of experimental results where the experimental results are unknown or uncertain by experimental method. This method may be used for predicting absorption energies of larger organic molecules that are unavailable by experimental methods and by high-accuracy theoretical methods with larger basis sets. The performance of this method was demonstrated by application to the absorption energy of the aldehyde carbazole precursor.