title: An effective method for accurate prediction of the first hyperpolarizability of alkalides.
abstract: The proper theoretical calculation method for nonlinear optical (NLO) properties is a key factor to design the excellent NLO materials. Yet it is a difficult task to obatin the accurate NLO property of large scale molecule. In present work, an effective intelligent computing method, as called extreme learning machine-neural network (ELM-NN), is proposed to predict accurately the first hyperpolarizability (β(0)) of alkalides from low-accuracy first hyperpolarizability. Compared with neural network (NN) and genetic algorithm neural network (GANN), the root-mean-square deviations of the predicted values obtained by ELM-NN, GANN, and NN with their MP2 counterpart are 0.02, 0.08, and 0.17 a.u., respectively. It suggests that the predicted values obtained by ELM-NN are more accurate than those calculated by NN and GANN methods. Another excellent point of ELM-NN is the ability to obtain the high accuracy level calculated values with less computing cost. Experimental results show that the computing time of MP2 is 2.4-4 times of the computing time of ELM-NN. Thus, the proposed method is a potentially powerful tool in computational chemistry, and it may predict β(0) of the large scale molecules, which is difficult to obtain by high-accuracy theoretical method due to dramatic increasing computational cost.
authors: Institute of Functional Material Chemistry, Faculty of Chemistry, Northeast Normal University, Changchun 130024, Jilin, People's Republic of China.
authors: Jia-Nan
authors: JN
authors: Wang
authors: Hong-Liang
authors: HL
authors: Xu
authors: Shi-Ling
authors: SL
authors: Sun
authors: Ting
authors: T
authors: Gao
authors: Hong-Zhi
authors: HZ
authors: Li
authors: Hui
authors: H
authors: Li
authors: Zhong-Min
authors: ZM
authors: Su
mesh_terms: D008672:Metals, Alkali / Q000737:chemistry*; D016571:Neural Networks, Computer; D011789:Quantum Theory
chemical_list: D008672:Metals, Alkali