Enhancing Reduced Density Matrix Functional Theory Calculations by Coupling Orbital and Occupation Optimizations

TL;DR

This paper introduces a coupled optimization method for reduced density matrix functional theory that updates orbitals and occupations simultaneously, significantly improving convergence speed and stability over traditional decoupled methods.

Contribution

The work proposes a novel coupled optimization approach combining unitary and EBI methods, with effective preconditioning and line search, to enhance RDMFT calculations.

Findings

Outperforms decoupled methods in convergence speed and stability

Achieves convergence for large systems like C60 in 154 iterations

Demonstrates robustness across different molecules, basis sets, and functionals

Abstract

Reduced density matrix functional theory (RDMFT) calculations are usually implemented in a decoupled manner, where the orbital and occupation optimizations are repeated alternately. Typically, orbital updates are performed using the unitary optimization method, while occupations are optimized through the explicit-by-implicit (EBI) method. The EBI method addresses explicit constraints by incorporating implicit functions, effectively transforming constrained optimization scenarios into unconstrained minimizations. Although the unitary and EBI methods individually achieve robust performance in optimizing orbitals and occupations, respectively, the decoupled optimization methods often suffer from slow convergence and require dozens of alternations between the orbital and occupation optimizations. To address this issue, this work proposes a coupled optimization method that combines unitary and EBI optimizations to update orbitals and occupations simultaneously at each step. To achieve favorable convergence in coupled optimization using a simple first-order algorithm, an effective and efficient preconditioner and line search are further introduced. The superiority of the new method is demonstrated through numerous tests on different molecules, random initial guesses, different basis sets and different functionals. It outperforms all decoupled optimization methods in terms of convergence speed, convergence results and convergence stability. Even a large system like $\mathrm{C_{60}}$ can converge to $10^{-8}$ au in 154 iterations, which shows that the coupled optimization method can make RDMFT more practical and facilitate its wider application and further development.