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Molecular Physics
An International Journal at the Interface Between Chemistry and Physics
Volume 120, 2022 - Issue 19-20: Special Issue of Molecular Physics in Memory of Lutosław Wolniewicz
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Special Issue of Molecular Physics in Memory of Lutosław Wolniewicz

Analytic gradients for compressed multistate pair-density functional theory

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Article: e2110534 | Received 16 Feb 2022, Accepted 21 Jul 2022, Published online: 06 Sep 2022
 

Abstract

Photochemical reactions often involve states that are closely coupled due to near degeneracies, for example by proximity to conical intersections. Therefore, a multistate method is used to accurately describe these states; for example, ordinary perturbation theory is replaced by quasidegenerate perturbation theory. Multiconfiguration pair-density functional theory (MC-PDFT) provides an efficient way to approximate the full dynamical correlation energy of strongly correlated systems, and we recently proposed compressed multistate pair-density functional theory (CMS-PDFT) to treat closely coupled states. In the present paper, we report the implementation of analytic gradients for CMS-PDFT in both OpenMolcas and PySCF, and we illustrate the use of these gradients by applying the method to the excited states of formaldehyde and phenol.

GRAPHICAL ABSTRACT

Acknowledgments

The authors are grateful to David Yarkony and Christopher Malbon for providing the vertical excitation energy of the S1 state of phenol from their potential energy surface of Ref. [Citation63]. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The present work is supported by the National Science Foundation under grant CHE-2054723. R.L. acknowledges the Swedish Research Council (VR, Grant 2020-03182) for funding. T.R.S. acknowledges that this material is also based upon work supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. DGE 1746045, Project No. 00074041.

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