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Numerical Optimization of Density Functional Tight Binding Models: Application to Molecules Containing Carbon, Hydrogen, Nitrogen, and Oxygen.
Krishnapriyan, A; Yang, P; Niklasson, A M N; Cawkwell, M J.
Afiliación
  • Krishnapriyan A; Department of Materials Science and Engineering, Stanford University , Stanford, California 94305, United States.
  • Yang P; Theoretical Division, Los Alamos National Laboratory , Los Alamos, New Mexico 87545, United States.
  • Niklasson AMN; Theoretical Division, Los Alamos National Laboratory , Los Alamos, New Mexico 87545, United States.
  • Cawkwell MJ; Theoretical Division, Los Alamos National Laboratory , Los Alamos, New Mexico 87545, United States.
J Chem Theory Comput ; 13(12): 6191-6200, 2017 Dec 12.
Article en En | MEDLINE | ID: mdl-29039935
New parametrizations for semiempirical density functional tight binding (DFTB) theory have been developed by the numerical optimization of adjustable parameters to minimize errors in the atomization energy and interatomic forces with respect to ab initio calculated data. Initial guesses for the radial dependences of the Slater-Koster bond integrals and overlap integrals were obtained from minimum basis density functional theory calculations. The radial dependences of the pair potentials and the bond and overlap integrals were represented by simple analytic functions. The adjustable parameters in these functions were optimized by simulated annealing and steepest descent algorithms to minimize the value of an objective function that quantifies the error between the DFTB model and ab initio calculated data. The accuracy and transferability of the resulting DFTB models for the C, H, N, and O system were assessed by comparing the predicted atomization energies and equilibrium molecular geometries of small molecules that were not included in the training data from DFTB to ab initio data. The DFTB models provide accurate predictions of the properties of hydrocarbons and more complex molecules containing C, H, N, and O.

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: J Chem Theory Comput Año: 2017 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: J Chem Theory Comput Año: 2017 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos