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Density Functional Theory as a Data Science
The Chemical Record ( IF 7.0 ) Pub Date : 2019-12-13 , DOI: 10.1002/tcr.201900081
Takao Tsuneda 1
Affiliation  

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.

中文翻译:

密度泛函理论作为数据科学

从数据科学的角度,重点讨论了物理意义和半经验参数对密度泛函理论(DFT)的功能和物理校正的发展。这项审查表明,DFT交换关联功能是在许多严格的物理条件下开发的,除了一些最近的功能外,它还使半经验参数的数量减至最少。交换相关函数的主要物理更正也显示出明确的物理含义,而与函数无关,尽管它们不可避免地需要最小的半经验参数,具体取决于所结合的函数。因此,我们解释说,即使从数据科学的角度来看,具有物理校正的DFT功能也是经过物理验证的最复杂的目标功能。
更新日期:2019-12-13
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