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Improving the accuracy of density-functional theory calculation: The genetic algorithm and neural network approach
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10.1063/1.2715579
/content/aip/journal/jcp/126/14/10.1063/1.2715579
http://aip.metastore.ingenta.com/content/aip/journal/jcp/126/14/10.1063/1.2715579

Figures

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FIG. 1.

The structure of our neural networks.

Image of FIG. 2.
FIG. 2.

Calculated absorption energies vs experimental absorption energies for all . Part (a) is for raw calculated absorption energies from the DFT approach. Part (b) is for neural network corrected absorption energies for the BPN approach. Part (c) is for the combined neural network and genetic algorithm corrected absorption energies for the GANN approach. Triangles (▵) are for the training set and crosses (×) are for the testing set. Insets are the histograms for the differences between the experimental and calculated absorption energies. All values are in units of eV.

Tables

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Table I.

The structure of the 150 organic molecules.

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Table II.

The experimental absorption energies and the differences between the calculated and experimental values of (in eV).

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Table III.

rms deviation of , DFT-BPN, and DFT-GANN corrections (in eV).

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Table IV.

Optimized values of synaptic weights and ..

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/content/aip/journal/jcp/126/14/10.1063/1.2715579
2007-04-09
2014-04-20
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752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: Improving the accuracy of density-functional theory calculation: The genetic algorithm and neural network approach
http://aip.metastore.ingenta.com/content/aip/journal/jcp/126/14/10.1063/1.2715579
10.1063/1.2715579
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