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

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.

Submitted by 阎军 (yanj@red.semi.ac.cn) on 2010-08-17T01:33:38Z No. of bitstreams: 1 Correcting the systematic error of the density functional theory calculation.pdf: 187209 bytes, checksum: 692be22238bc711b969609e372eef80b (MD5)

Approved for entry into archive by 阎军(yanj@red.semi.ac.cn) on 2010-08-17T02:26:30Z (GMT) No. of bitstreams: 1 Correcting the systematic error of the density functional theory calculation.pdf: 187209 bytes, checksum: 692be22238bc711b969609e372eef80b (MD5)

Made available in DSpace on 2010-08-17T02:26:30Z (GMT). No. of bitstreams: 1 Correcting the systematic error of the density functional theory calculation.pdf: 187209 bytes, checksum: 692be22238bc711b969609e372eef80b (MD5) Previous issue date: 2010

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

国内

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