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. © 2011 Wiley Periodicals, Inc. J Comput Chem, 2011