Co-iterative augmented Hessian method for orbital optimization

Abstract

Orbital optimization procedure is widely called in electronic structure simulation. To efficiently find the orbital optimization solution, we developed a new second order orbital optimization algorithm, co-iteration augmented Hessian (CIAH) method. In this method, the orbital optimization is embedded in the diagonalization procedure for augmented Hessian (AH) eigenvalue equation. Approximations for Hessian matrix can be easily employed in this method to improve the computational costs. We numerically performed the CIAH algorithm with SCF convergence of 20 challenging systems and Boys localization of C60 molecule. We found that CIAH algorithm has better SCF convergence and less computational costs than direct inversion iterative subspace (DIIS) algorithm. The numerical tests suggest that CIAH is a stable, reliable and efficient algorithm for orbital optimization problem.

Cited by in corpus (11)

• A general second order complete active space self-consistent-field solver for large-scale systems
• Orbital transformations to reduce the 1-norm of the electronic structure Hamiltonian for quantum computing applications
• The Python-based Simulations of Chemistry Framework (PySCF)
• PyFLOSIC: Python-based Fermi-Löwdin orbital self-interaction correction
• Predicting The One-Particle Density Matrix With Machine Learning
• Economical Quasi-Newton Self Consistent Field Solver
• Limitations of Hartree-Fock with quantum resources
• Bond formation insights into the Diels-Alder reaction: A bond perception and self-interaction perspective
• Impact of Parametrizations of the One-Body Reduced Density Matrix on the Energy Landscape
• iCISCF: An Iterative Configuration Interaction-based Multiconfigurational Self-consistent Field Theory for Large Active Spaces
• $i$CAS: Imposed Automatic Selection and Localization of Complete Active Spaces