title: Improving the accuracy of density-functional theory calculation: the genetic algorithm and neural network approach.
abstract: The combination of genetic algorithm and neural network approach (GANN) has been developed to improve the calculation accuracy of density functional theory. As a demonstration, this combined quantum mechanical calculation and GANN correction approach has been applied to evaluate the optical absorption energies of 150 organic molecules. The neural network approach reduces the root-mean-square (rms) deviation of the calculated absorption energies of 150 organic molecules from 0.47 to 0.22 eV for the TDDFTB3LYP6-31G(d) calculation, and the newly developed GANN correction approach reduces the rms deviation to 0.16 eV.
authors: Institute of Functional Material Chemistry, Faculty of Chemistry, Northeast Normal University, Changchun 130024, People's Republic of China.
authors: Hui
authors: H
authors: Li
authors: LiLi
authors: L
authors: Shi
authors: Min
authors: M
authors: Zhang
authors: Zhongmin
authors: Z
authors: Su
authors: XiuJun
authors: X
authors: Wang
authors: LiHong
authors: L
authors: Hu
authors: GuanHua
authors: G
authors: Chen
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D004735:Energy Transfer; D008956:Models, Chemical; D008957:Models, Genetic; D008958:Models, Molecular; D016571:Neural Networks, Computer; D009930:Organic Chemicals / Q000737:chemistry*
chemical_list: D009930:Organic Chemicals