A Story of Three Levels of Sophistication in SCF/KS-DFT Orbital Optimization Procedures.

ABSTRACT: In this work, three versions of self-consistent field/Kohn-Sham density functional theory (SCF/KS-DFT) orbital optimization are described and benchmarked. The methods are a modified version of the geometry version of the direct inversion in the iterative subspace approach (which we call r-GDIIS), the modified restricted step rational function optimization method (RS-RFO), and the novel subspace gradient-enhanced Kriging method combined with restricted variance optimization (S-GEK/RVO). The modifications introduced are aimed at improving the robustness and computational scaling of the procedures. In particular, the subspace approach in S-GEK/RVO allows the application to SCF/KS-DFT optimization of a machine learning technique that has proven to be successful in geometry optimizations. The p

SUBMITTER: Sethio D 

PROVIDER: S-EPMC10983011 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature