Improving the Silicon Interactions of GFN-xTB

J Chem Inf Model. 2021 Dec 27;61(12):5931-5937. doi: 10.1021/acs.jcim.1c01170. Epub 2021 Dec 10.

Abstract

A general-purpose density functional tight binding method, the GFN-xTB model is gaining increased popularity in accurate simulations that are out of scope for conventional ab initio formalisms. We show that in its original GFN1-xTB parametrization, organosilicon compounds are described poorly. This issue is addressed by re-fitting the model's silicon parameters to a data set of 10 000 reference compounds, geometry-optimized with the revPBE functional. The resulting GFN1(Si)-xTB parametrization shows improved accuracy in the prediction of system energies, nuclear forces, and geometries and should be considered for all applications of the GFN-xTB Hamiltonian to systems that contain silicon.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Quantum Theory*
  • Silicon*

Substances

  • Silicon