Correcting the systematic error of the density functional theory calculation: the alternate combination approach of genetic algorithm and neural network |
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| Authors: | Wang Ting-Ting Li Wen-Long Chen Zhang-Hui and Miao Ling |
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| Affiliation: | Department of Electronic Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China; State Key Laboratory for Superlattices and Microstructures, Institute of Semiconductors, Chinese Academy of Sciences,Beijing 100083, China |
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| Abstract: | The alternate combinational approach of genetic algorithm and neural network (AGANN) has been presented to correct the systematic error of the density functional theory (DFT) calculation. It treats the DFT as a black box and models the error through external statistical information. As a demonstration, the AGANN method has been applied in the correction of the lattice energies from the DFT calculation for 72 metal halides and hydrides. Through the AGANN correction, the mean absolute value of the relative errors of the calculated lattice energies to the experimental values decreases from 4.93% to 1.20% in the testing set. For comparison, the neural network approach reduces the mean value to 2.56%. And for the common combinational approach of genetic algorithm and neural network, the value drops to 2.15%. The multiple linear regression method almost has no correction effect here. |
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| Keywords: | density functional theory neural network genetic algorithm alternate combination |
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