Energy Landscape of State-Specific Electronic Structure Theory

TL;DR

This paper introduces a geometric perspective on the exact electronic energy landscape, revealing how ground and excited states are connected and how this landscape influences state-specific approximations and variance optimization challenges.

Contribution

It provides a novel geometric framework for understanding the structure of the electronic energy landscape and its impact on state-specific electronic structure methods.

Findings

Exact energy landscape features stationary points for ground and excited states.

Hessian index increases with excitation level, indicating more complex landscape topology.

Variance optimization faces challenges due to unphysical saddle points and maxima.

Abstract

State-specific approximations can provide an accurate representation of challenging electronic excitations by enabling relaxation of the electron density. While state-specific wave functions are known to be local minima or saddle points of the approximate energy, the global structure of the exact electronic energy remains largely unexplored. In this contribution, a geometric perspective on the exact electronic energy landscape is introduced. On the exact energy landscape, ground and excited states form stationary points constrained to the surface of a hypersphere and the corresponding Hessian index increases at each excitation level. The connectivity between exact stationary points is investigated and the square-magnitude of the exact energy gradient is shown to be directly proportional to the Hamiltonian variance. The minimal basis Hartree-Fock and Excited-State Mean-Field representations of singlet H$_2$ (STO-3G) are then used to explore how the exact energy landscape controls the existence and properties of state-specific approximations. In particular, approximate excited states correspond to constrained stationary points on the exact energy landscape and their Hessian index also increases for higher energies. Finally, the properties of the exact energy are used to derive the structure of the variance optimisation landscape and elucidate the challenges faced by variance optimisation algorithms, including the presence of unphysical saddle points or maxima of the variance.