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February 8, 20260 citations

Efficient all-electron periodic Fourier-transformed Coulomb method.

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HDHieu Q. DinhARAdam RettigXFX.L. Feng

Key Points

  • The research aims to create a more efficient algorithm for calculating periodic Coulomb matrices in solid-state systems.
  • Utilized Ewald summation for short-range contributions evaluated in real space.
  • Implemented Fourier-transformed Coulomb method for long-range contributions.
  • Introduced integral-direct plane wave density fitting for both compact and diffuse densities.
  • Applied dispersion-corrected PBE functional using Dunning and Karlsruhe basis sets.
  • Achieved speedups for solid-state systems compared to the range-separated density fitting method.
  • Computed cohesive energy of the benzene crystal with good agreement to literature values.
  • Determined the adsorption energy of CO on the MgO(001) surface with accurate results.

Abstract

We present an efficient algorithm for constructing an all-electron periodic Coulomb matrix based on Ewald summation combined with the Fourier-transformed Coulomb method. The short-range contributions involving compact densities are evaluated in real space using standard Gaussian density fitting. For the long-range contributions, we introduce an integral-direct plane wave density fitting scheme that is applicable to both compact and diffuse densities. The resulting method achieves orders-of-magnitude speedups for prototypical solid-state systems compared to a closely related approach, the range-separated density fitting method. Using the dispersion-corrected PBE functional with all-electron Dunning and Karlsruhe basis sets, we apply our method to compute the cohesive energy of the benzene crystal and the adsorption energy of CO on the MgO(001) surface. These results are in good agreement with existing literature. Our approach enables efficient Gaussian-based semi-local density functional calculations using dense k-point meshes and traditional molecular Gaussian basis sets.

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Cite This Study

Dinh et al. (2026) studied this question.

synapsesocial.com/papers/698828fd0fc35cd7a8848fdbhttps://doi.org/10.1063/5.0303084
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