Literature DB >> 29039935

Numerical Optimization of Density Functional Tight Binding Models: Application to Molecules Containing Carbon, Hydrogen, Nitrogen, and Oxygen.

A Krishnapriyan1,2, P Yang2, A M N Niklasson2, M J Cawkwell2.   

Abstract

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.

Entities:  

Year:  2017        PMID: 29039935     DOI: 10.1021/acs.jctc.7b00762

Source DB:  PubMed          Journal:  J Chem Theory Comput        ISSN: 1549-9618            Impact factor:   6.006


  2 in total

1.  Examining the chemical and structural properties that influence the sensitivity of energetic nitrate esters.

Authors:  Virginia W Manner; Marc J Cawkwell; Edward M Kober; Thomas W Myers; Geoff W Brown; Hongzhao Tian; Christopher J Snyder; Romain Perriot; Daniel N Preston
Journal:  Chem Sci       Date:  2018-03-09       Impact factor: 9.825

2.  PEPCONF, a diverse data set of peptide conformational energies.

Authors:  Viki Kumar Prasad; Alberto Otero-de-la-Roza; Gino A DiLabio
Journal:  Sci Data       Date:  2019-01-22       Impact factor: 6.444

  2 in total

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