4.6 Article

Density Functional Theory as a Data Science

期刊

CHEMICAL RECORD
卷 20, 期 7, 页码 618-639

出版社

WILEY-V C H VERLAG GMBH
DOI: 10.1002/tcr.201900081

关键词

Density functional theory; Exchange-correlation functionals; Physical corrections; Machine learning

资金

  1. Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT) [17H01188, 16KT0047]
  2. Grants-in-Aid for Scientific Research [16KT0047, 17H01188] Funding Source: KAKEN

向作者/读者索取更多资源

The development of density functional theory (DFT) functionals and physical corrections are reviewed focusing on the physical meanings and the semiempirical parameters from the viewpoint of data science. This review shows that DFT exchange-correlation functionals have been developed under many strict physical conditions with minimizing the number of the semiempirical parameters, except for some recent functionals. Major physical corrections for exchange-correlation function- als are also shown to have clear physical meanings independent of the functionals, though they inevitably require minimum semiempirical parameters dependent on the functionals combined. We, therefore, interpret that DFT functionals with physical corrections are the most sophisticated target functions that are physically legitimated, even from the viewpoint of data science.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据