Spectral finite-element formulation of the optimized effective potential method for atomic structure in the random phase approximation

We present a spectral finite-element formulation of the optimized effective potential (OEP) method for atomic structure calculations in the random phase approximation (RPA). In particular, we develop a finite-element framework that employs a polynomial mesh with element nodes placed according to the Chebyshev-Gauss-Lobatto scheme, high-order C0-continuous Lagrange polynomial basis functions, and Gauss-Legendre quadrature for spatial integration. We employ distinct polynomial degrees for the orbitals, Hartree potential, and RPA-OEP exchange-correlation potential. Through representative examples, we verify the accuracy of the developed framework, assess the fidelity of one-parameter double-hybrid functionals constructed with RPA correlation, and develop a machine-learned model for the RPA-OEP exchange-correlation potential at the level of the generalized gradient approximation, based on the kernel method and linear regression.

Publication Details

Journal
The Journal of Chemical Physics
Published
2026-02-23
DOI
https://doi.org/10.1063/5.0318188
Primary Topic
Machine Learning in Materials Science
Type
article
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article

Spectral finite-element formulation of the optimized effective potential method for atomic structure in the random phase approximation

The Journal of Chemical Physics
Machine Learning in Materials Science
article

Spectral finite-element formulation of the optimized effective potential method for atomic structure in the random phase approximation

article en

Abstract

We present a spectral finite-element formulation of the optimized effective potential (OEP) method for atomic structure calculations in the random phase approximation (RPA). In particular, we develop a finite-element framework that employs a polynomial mesh with element nodes placed according to the Chebyshev-Gauss-Lobatto scheme, high-order C0-continuous Lagrange polynomial basis functions, and Gauss-Legendre quadrature for spatial integration. We employ distinct polynomial degrees for the orbitals, Hartree potential, and RPA-OEP exchange-correlation potential. Through representative examples, we verify the accuracy of the developed framework, assess the fidelity of one-parameter double-hybrid functionals constructed with RPA correlation, and develop a machine-learned model for the RPA-OEP exchange-correlation potential at the level of the generalized gradient approximation, based on the kernel method and linear regression.

The Journal of Chemical PhysicsVol. 164(8)
Openalex Percentile: Top 47%
Machine Learning in Materials Science
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