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

TL;DR

This paper introduces an adaptive backtracking line search algorithm for self-consistent field iterations in density-functional theory, which automatically adjusts damping to improve convergence across diverse challenging systems.

Contribution

A new automatic damping method using a backtracking line search based on a theoretical energy model, eliminating the need for user-selected damping parameters.

Findings

Successfully applied to complex systems like supercells and transition-metal alloys.

Improves convergence robustness and efficiency in SCF iterations.

Eliminates manual damping parameter tuning.

Abstract

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 damped 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.