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improving the accuracy of density-functional theory calculation:The genetic algorithm and neural network approach

机译:提高密度泛函理论计算的准确性:遗传算法和神经网络方法

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摘要

The combination of genetic algorithm and neural network approach(GANN)has been developed to improve the calculation accuracy of density functional theory.As a demonstration,this combined quantum mechanical calculation and GANN correction approach has been applied to evaluate the optical absorption energies of 150 organic molecules.The neural network approach reduces the root-mean-square(rms).deviation of the calculated absorption energies of 150 organic molecules from 0.47 to 0.22 eV for the TDDFT/B3LYP/6-31G(d)calculation,and the newly developed GANN correction approach reduces the rms deviation to 0.16 eV.
机译:为了提高密度泛函理论的计算精度,开发了遗传算法和神经网络方法(GANN)的结合。作为演示,此量子力学计算和GANN校正方法相结合被用于评估150种有机物的光吸收能神经网络方法减少了均方根(rms)。对于TDDFT / B3LYP / 6-31G(d)计算,150种有机分子的计算吸收能从0.47到0.22 eV的偏差,以及最新开发的GANN校正方法可将均方根偏差降低至0.16 eV。

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