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Exact Mo̷ller-Plesset Adiabatic Connection Correlation Energy Densities.

Kimberly J Daas1, Heng Zhao2, Elias Polak2

  • 1Department of Chemistry, University of California, Irvine, California 92697, United States.

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|May 21, 2025
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Summary
This summary is machine-generated.

We introduce a wave function-based correlation energy density using the Mo̷ller-Plesset adiabatic connection (MPAC). This new method bridges wave function and density functional theory (DFT) for electronic structure calculations.

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Area of Science:

  • Quantum Chemistry
  • Computational Chemistry
  • Electronic Structure Theory

Background:

  • The Mo̷ller-Plesset adiabatic connection (MPAC) is crucial for developing density functional theory (DFT)-like approximations.
  • Mapping Hartree-Fock (HF) densities to wave function-based correlation energy leverages both DFT and wave function concepts.
  • Correlation energy density is well-studied in DFT but largely unexplored in wave function theory.

Purpose of the Study:

  • Introduce a rigorous formulation of wave function-based correlation energy density within MPAC.
  • Implement and analyze this quantity for small atomic and diatomic systems.
  • Explore its potential for developing new electronic structure approximations.

Main Methods:

  • Formulation of wave function-based correlation energy density using a general gauge strategy.
  • Implementation via full configuration interaction (FCI) calculations.
  • Derivation of the MP2 limit in terms of HF orbitals.

Main Results:

  • A rigorous definition and implementation of wave function-based correlation energy density.
  • Analysis of its behavior and contributions in small systems.
  • Identification of commonalities and differences with DFT correlation energy density.
  • Derivation of the MP2 limit.

Conclusions:

  • The developed wave function-based correlation energy density offers a new perspective on electronic structure.
  • It provides a bridge between DFT and wave function methods.
  • These energy densities can serve as targets for new machine learning and traditional electronic structure approximations.