...
首页> 外文期刊>Journal of molecular modeling >Franck–Condon factors using supervised artificial neural networks. I. The CF~+ cation
【24h】

Franck–Condon factors using supervised artificial neural networks. I. The CF~+ cation

机译:使用监督人工神经网络的Franck–Condon因子。 I. CF〜+阳离子

获取原文
获取原文并翻译 | 示例
   

获取外文期刊封面封底 >>

       

摘要

Several studies of the electronic and vibrational structure of CF~+ have been performed since this molecule was first discovered to occur in the interstellar medium, and even before that. However, researchers have paid little attention to calculating its Franck–Condon factors (FCFs), which can aid the identification of this molecule through comparison with the observed intensity spectrum. In this work, an analysis of all of the potential energy curves of CF~+ that were candidates for this kind of calculation was undertaken. The Franck–Condon factors of CF~+ were calculated using a supervised neural network with two layers and a variable learning rate.
机译:自从首次发现该分子存在于星际介质中以来,就对CF〜+的电子和振动结构进行了几项研究。但是,研究人员很少关注其弗兰克-康登因子(FCF)的计算,这可以通过与观察到的强度谱进行比较来帮助鉴定该分子。在这项工作中,对所有可能的CF〜+势能曲线进行了分析。 CF〜+的Franck-Condon因子是使用具有两层且学习率可变的监督神经网络计算的。

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