Energy-adaptive Riemannian conjugate gradient method for density functional theory
Abstract
This paper presents a novel Riemannian conjugate gradient method for the Kohn-Sham energy minimization problem in density functional theory (DFT), with a focus on non-metallic crystal systems. We introduce an energy-adaptive metric that preconditions the Kohn-Sham model, significantly enhancing optimization efficiency. Additionally, a carefully designed shift strategy and several algorithmic improvements make the implementation comparable in performance to highly optimized self-consistent field iterations. The energy-adaptive Riemannian conjugate gradient method has a sound mathematical foundation, including stability and convergence, offering a reliable and efficient alternative for DFT-based electronic structure calculations in computational chemistry.
Fields of Study & Subjects
Numerical methods in inverse problems
Thermoelastic and Magnetoelastic Phenomena
Iterative Methods for Nonlinear Equations
Publication details
- Journal / source
- Journal of Computational Physics
- Publisher
- Elsevier BV
- Volume
- 566
- Pages
- 115226–115226
- Publication date
- 22 July 2026
- Language
- EN