4.7 Article

Formulation and Implementation of Density Functional Embedding Theory Using Products of Basis Functions

期刊

JOURNAL OF CHEMICAL THEORY AND COMPUTATION
卷 17, 期 7, 页码 3995-4005

出版社

AMER CHEMICAL SOC
DOI: 10.1021/acs.jctc.1c00175

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资金

  1. Swiss National Science Foundation (SNSF) [PZ00P2_174227]
  2. Swiss National Supercomputing Centre (CSCS) [uzh1]
  3. Swiss National Science Foundation (SNF) [PZ00P2_174227] Funding Source: Swiss National Science Foundation (SNF)

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The research on embedding potential using products of atomic orbital basis functions offers a new approach in the context of density functional embedding theory, allowing for the treatment of pseudopotential and all-electron calculations in a compact matrix form. With cost reduction procedures and population analysis based potential reduction, the method provides a simplified way to handle basis sets and potentials. Implemented for various systems, including proton-transfer reactions and density of states calculations, the method shows potential for large-scale applications to extended systems.
The representation of embedding potential using products of atomic orbital basis functions has been developed in the context of density functional embedding theory. The formalism allows to treat pseudopotential and all-electron calculations on the same footing and enables simple transfer of the embedding potential in a compact matrix form. In addition, a cost-reduction procedure for the basis set and potential reduction based on population analysis has been proposed. Implemented for the condensed-phase and molecular systems within Gaussian and plane-waves and Gaussian and augmented plane-waves formalisms, the scheme has been tested for proton-transfer reactions in the cluster and the condensed phase and projected density of states of carbon monoxide adsorbed on platinum surface. With the computational scaling of the embedding potential optimization similar to that of hybrid density functional theory with a significantly reduced prefactor, the method allows for large-scale applications to extended systems.

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