Analytic gradients for multiconfiguration pair-density functional theory with density fitting: Development and application to geometry optimization in the ground and excited states

Thais R. Scott, Meagan S. Oakley, Matthew R. Hermes, Andrew M. Sand, Roland Lindh, Donald G. Truhlar, Laura Gagliardi

Research output: Contribution to journalArticlepeer-review

Abstract

Density fitting reduces the computational cost of both energy and gradient calculations by avoiding the computation and manipulation of four-index electron repulsion integrals. With this algorithm, one can efficiently optimize the geometries of large systems with an accurate multireference treatment. Here, we present the derivation of multiconfiguration pair-density functional theory for energies and analytic gradients with density fitting. Six systems are studied, and the results are compared to those obtained with no approximation to the electron repulsion integrals and to the results obtained by complete active space second-order perturbation theory. With the new approach, there is an increase in the speed of computation with a negligible loss in accuracy. Smaller grid sizes have also been used to reduce the computational cost of multiconfiguration pair-density functional theory with little effect on the optimized geometries and gradient values.

Original languageEnglish (US)
Article number074108
JournalJournal of Chemical Physics
Volume154
Issue number7
DOIs
StatePublished - Feb 21 2021

Bibliographical note

Funding Information:
T.R.S. acknowledges that this material is based on the work supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. CON-75851, Project No. 00074041. 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. This work was supported in part by the Air Force Office of Scientific Research under Grant No. FA9550-16-1-0134. The authors acknowledge the Minnesota Supercomputing Institute (MSI) at the University of Minnesota for providing resources that contributed to the research results reported here. R.L. acknowledges funding from the Swedish Research Council (Grant No. 2016-03398).

Publisher Copyright:
© 2021 Author(s).

PubMed: MeSH publication types

  • Journal Article

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