σ-SCF: A direct energy-targeting method to mean-field excited states
Author(s)
Ye, Hongzhou; Welborn, Matthew Gregory; Ricke, Nathan Darrell; Van Voorhis, Troy
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The mean-field solutions of electronic excited states are much less accessible than ground state (e.g., Hartree-Fock) solutions. Energy-based optimization methods for excited states, like ∆-SCF (selfconsistent
field), tend to fall into the lowest solution consistent with a given symmetry—a problem known as “variational collapse.” In this work, we combine the ideas of direct energy-targeting and variance-based optimization in order to describe excited states at the mean-field level. The resulting method, σ-SCF, has several advantages. First, it allows one to target any desired excited state by specifying a single parameter: a guess of the energy of that state. It can therefore, in principle, find all excited states. Second, it avoids variational collapse by using a variance-based, unconstrained local minimization. As a consequence, all states—ground or excited—are treated on an equal footing. Third, it provides an alternate approach to locate ∆-SCF solutions that are otherwise hardly accessible
by the usual non-aufbau configuration initial guess. We present results for this new method for small
atoms (He, Be) and molecules (H2, HF). We find that σ-SCF is very effective at locating excited states, including individual, high energy excitations within a dense manifold of excited states. Like all single determinant methods, σ-SCF shows prominent spin-symmetry breaking for open shell states
and our results suggest that this method could be further improved with spin projection
Date issued
2017-12Department
Massachusetts Institute of Technology. Department of ChemistryJournal
The Journal of Chemical Physics
Publisher
American Institute of Physics (AIP)
Citation
Ye, Hong-Zhou, Matthew Welborn, Nathan D. Ricke, and Troy Van Voorhis. “σ-SCF: A Direct Energy-Targeting Method to Mean-Field Excited States.” The Journal of Chemical Physics 147, no. 21 (December 7, 2017): 214104. © 2018 AIP Publishing LLC
Version: Final published version
ISSN
0021-9606
1089-7690