Year: 2025
Type: article
Abstract: For any linear system with unreduced dynamics governed by invertible propagators, we derive a closed, time-delayed, linear system for a reduced-dimensional quantity of interest. This method does not target dimensionality reduction: rather, this method helps shed light on the memory-dependence of 1-electron reduced density matrices in time-dependent configuration interaction (TDCI), a scheme to solve for the correlated dynamics of electrons in molecules. Though time-dependent density functional theory has established that the 1-electron reduced density possesses memory-dependence, the precise nature of this memory-dependence has not been understood. We derive a symmetry/constraint-preserving method to propagate reduced TDCI electron density matrices. In numerical tests on two model systems (H2 and HeH+), we show that with sufficiently large time-delay (or memory-dependence), our method propagates reduced TDCI density matrices with high quantitative accuracy. We study the dependence of our results on time step and basis set. To implement our method, we derive the 4-index tensor that relates reduced and full TDCI density matrices. Our derivation applies to any TDCI system, regardless of basis set, number of electrons, or choice of Slater determinants in the wave function.
Source: Journal of Mathematical Physics
DOI: 10.1063/5.0223327
Institution: University of California, Merced
Language: English
Subfield: Atomic and Molecular Physics, and Optics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
Funders: National Science Foundation, U.S. Department of Energy, National Energy Research Scientific Computing Center, Office of Science, University of California Merced, Office of Naval Research, Office of Advanced Cyberinfrastructure, Division of Mathematical Sciences, Basic Energy Sciences, Advanced Scientific Computing Research, Army Research Office, Lawrence Berkeley National Laboratory
Awards: G1079818582, G1143651532, G122673668, G1286236842, G1286251239, G1314494723, G1677143136, G1799333409, G1863003419, G30714459, G3083819904, G3625083078, G4087935488, G419881185, G42916606, MRI: Acquisition of Pinnacles -- Raising Research Computing to New Heights in California's Central Valley, G4501827968, G4778302020, G4801093503, G498139845, G5076365615, G5261056217, APPLYING DEEP LEARNING METHODS TO DEVELOP NEW MODELS OF CHARGE TRANSFER, NONADIABATIC DYNAMICS, AND NONLINEAR SPECTROSCOPY IN THE CONDENSED PHASE, G5328358057, G5588225710, G6097507954, APPLYING DEEP LEARNING METHODS TO DEVELOP NEW MODELS OF CHARGE TRANSFER, NONADIABATIC DYNAMICS, AND NONLINEAR SPECTROSCOPY IN THE CONDENSED PHASE, G650090426, G7354628648, G7452299184, RTG: Data-Intensive Research and Computing at the University of California, Merced, G969889393
OpenAlex ID: W4407162072