Correcting the systematic error of the density functional theory calculation:the alternate combination approach of genetic algorithm and neural network  

作　　者：王婷婷 李文龙 陈章辉 缪灵 

机构地区：[1]Department of Electronic Science and Technology,Huazhong University of Science and Technology [2]State Key Laboratory for Superlattices and Microstructures,Institute of Semiconductors,Chinese Academy of Sciences

出　　处：《Chinese Physics B》2010年第7期437-444,共8页中国物理B（英文版）

基　　金：supported by the National Basic Research Program of China (973 Program) (Grant No. G2009CB929300);the National Natural Science Foundation of China (Grant No. 60521001 and 60925016)

摘　　要：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 ACANN 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.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 ACANN 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.

关 键 词：density functional theory neural network genetic algorithm alternate combination 

分 类 号：O469[理学—凝聚态物理]