Near-Exact Nuclear Gradients of Complete Active Space Self-Consistent Field Wave Functions

TL;DR

This paper introduces a method for calculating nuclear gradients in HCISCF wave functions, enabling efficient geometry optimization of strongly correlated molecules with larger active spaces, and compares results with DFT for Fe(PDI).

Contribution

The paper presents a near-exact nuclear gradient approach for HCISCF wave functions, reducing computational cost while maintaining accuracy, and demonstrates its application to complex systems like Fe(PDI).

Findings

HCISCF nuclear gradients are insensitive to variational space size.

HCISCF effectively treats larger active spaces for strongly correlated systems.

Multiple near-degenerate minima found on Fe(PDI) triplet surface.

Abstract

In this paper, we study the nuclear gradients of heat bath configuration interaction self-consistent field (HCISCF) wave functions and use them to optimize molecular geometries for various molecules. We show that the HCISCF nuclear gradients are fairly insensitive to the size of the "selected" variational space, which allows us to reduce the computational cost without introducing significant error. The ability of HCISCF to treat larger active spaces combined with the flexibility for users to control the computational cost makes the method very attractive for studying strongly correlated systems which require a larger active space than possible with complete active space self-consistent field (CASSCF). Finally, we study the realistic catalyst, Fe(PDI), and highlight some of the challenges this system poses for density functional theory (DFT). We demonstrate how HCISCF can clarify the energetic stability of geometries obtained from DFT when the results are strongly dependent on the functional. We also use the HCISCF gradients to optimize geometries for this species and study the adiabatic singlet-triplet gap. During geometry optimization, we find that multiple near-degenerate local minima exist on the triplet potential energy surface.