The Importance of being consistent

TL;DR

This paper emphasizes the critical role of self-consistency in density functional theory (DFT), analyzing how errors originate from approximate functionals versus densities, and demonstrating that improving densities can significantly reduce errors.

Contribution

It provides a comprehensive analysis of self-consistency in various DFT methods, offering new insights into error sources and strategies for error reduction.

Findings

Errors often stem from approximate densities rather than functionals.

Using better densities can substantially reduce DFT errors.

The analysis applies to Kohn-Sham, orbital-free DFT, and Partition-DFT.

Abstract

We review the role of self-consistency in density functional theory. We apply a recent analysis to both Kohn-Sham and orbital-free DFT, as well as to Partition-DFT, which generalizes all aspects of standard DFT. In each case, the analysis distinguishes between errors in approximate functionals versus errors in the self-consistent density. This yields insights into the origins of many errors in DFT calculations, especially those often attributed to self-interaction or delocalization error. In many classes of problems, errors can be substantially reduced by using `better' densities. We review the history of these approaches, many of their applications, and give simple pedagogical examples.