首页> 外文期刊>The Journal of Chemical Physics >Improving the accuracy of density-functional theory calculation: The genetic algorithm and neural network approach
【24h】

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

获取原文
获取原文并翻译 | 示例
           

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. (c) 2007 American Institute of Physics.
机译:遗传算法和神经网络方法(GANN)的结合已经被开发出来,以提高密度泛函理论的计算精度。作为演示,此组合的量子力学计算和GANN校正方法已应用于评估150种有机分子的光吸收能。对于TDDFT / B3LYP / 6-31G(d)计算,神经网络方法将150种有机分子的计算出的吸收能量的均方根(rms)偏差从0.47降低到0.22 eV,以及新开发的GANN校正方法降低均方根误差至0.16 eV。 (c)2007年美国物理研究所。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号