A robust and efficient line search for self-consistent field iterations

We propose a novel adaptive damping algorithm for the self-consistent field (SCF) iterations of Kohn-Sham density-functional theory, using a backtracking line search to automatically adjust the damping in each SCF step. This line search is based on a theoretically sound, accurate and inexpensive model for the energy as a function of the damping parameter. In contrast to usual SCF schemes, the resulting algorithm is fully automatic and does not require the user to select a damping. We successfully apply it to a wide range of challenging systems, including elongated supercells, surfaces and transition-metal alloys. (c) 2022 Elsevier Inc. All rights reserved.

Automated summary

In this study, a novel adaptive damping algorithm for self-consistent field (SCF) iterations in Kohn-Sham density-functional theory is proposed. The algorithm adjusts the damping in each SCF step using a backtracking line search based on a theoretically sound and accurate energy model. Unlike traditional SCF schemes, this algorithm is fully automatic and does not require user input for selecting the damping parameter. The algorithm is successfully applied to various challenging systems, including elongated supercells, surfaces, and transition-metal alloys.