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SPC cluster modeling of metal oxides: ways of determining the values of point charges in the embedded cluster model


Several criteria for determining self-consistently the magnitude of point charges employed in the embedded cluster modeling of metal oxides have been proposed and investigated. Merits and demerits of these criteria have been compared.Ab initio study has been performed to show the influence of the values of point charges chosen on the calculated electronic properties of the embedded MgO cluster. The calculation results demonstrate that the electronic properties of the embedded cluster are of great dependence on the magnitude of the embedding point charges; that the employment of the nominal charges, ±2.0, would cause overestimation of the crystal potential even in the case of the so-called purely ionic oxide. MgO; and that certain requirements for the consistence between the embedded cluster and the embedding point charges should be reached. It is further found that errors for the calculated properties of the embedded cluster still exist with respect to those of bulk solid even in the case that self-consistence in terms of charge, dipole moment, or electrostatic potential was met between the cut-out cluster and the embedding point charges. As far as spherical expansion is performed upon the embedding point charges, which furnishes the embedding point charges with a continuous distribution of charge density, a global agreement is reached between the calculated properties of the embedded cluster model and those of the bulk solid.

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Project supported by the National Natural Science Foundation of China, the specific doctoral project foundation sponsored by the State Education Commission of China, and Japan Society for Promotion of Science

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Xu, X., Nakatsuji, H., Ehara, M. et al. SPC cluster modeling of metal oxides: ways of determining the values of point charges in the embedded cluster model. Sc. China Ser. B-Chem. 41, 113–121 (1998).

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  • metal oxides
  • duster-surface analogy
  • embedded cluster model
  • SPC cluster model