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  5. Compressed-State Multistate Pair-Density Functional Theory

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Article
en
2020

Compressed-State Multistate Pair-Density Functional Theory

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en
2020
Vol 16 (12)
Vol. 16
DOI: 10.1021/acs.jctc.0c00908

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Donald G Truhlar
Donald G Truhlar

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Jie J. Bao
Chen Zhou
Donald G Truhlar

Abstract

Multiconfiguration pair-density functional theory (MC-PDFT) is a multireference method that can be used to calculate excited states. However, MC-PDFT potential energy surfaces have the wrong topology at conical intersections because the last step of MC-PDFT is not a diagonalization of a model-space Hamiltonian matrix, as done in, for example, multistate second-order perturbation theory (MS-CASPT2). We have previously proposed methods that solve this problem by diagonalizing a model-space effective Hamiltonian matrix, where the diagonal elements are MC-PDFT energies for intermediate states, and the off-diagonal elements are evaluated by wave function theory. One previous method is called variational multistate PDFT (VMS-PDFT), whose intermediate states maximize the trace of the effective Hamiltonian, namely, the sum of the MC-PDFT energies of the model-space states; the VMS-PDFT is very robust but is more computationally expensive than another method, extended multistate PDFT (XMS-PDFT), in which the transformation to intermediate states is accomplished without needing any density functional evaluations. However, although VMS-PDFT was accurate in all cases tested, XMS-PDFT was accurate in only some of them. In the present paper, we propose a new method, called compressed-state multistate PDFT (CMS-PDFT), that is as efficient as XMS-PDFT and as accurate as VMS-PDFT. The new method maximizes the trace of the classical Coulomb energy of the intermediate states such that the electron densities of the intermediate states are compressed. We show that CMS-PDFT performs robustly even where XMS-PDFT fails.

How to cite this publication

Jie J. Bao, Chen Zhou, Donald G Truhlar (2020). Compressed-State Multistate Pair-Density Functional Theory. , 16(12), DOI: https://doi.org/10.1021/acs.jctc.0c00908.

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Publication Details

Type

Article

Year

2020

Authors

3

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.1021/acs.jctc.0c00908

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