AI Article Synopsis
- The GFN-xTB model is a density functional tight binding method that is increasingly used for simulations that traditional models cannot handle.
- In its original form, the GFN1-xTB model does not accurately describe organosilicon compounds.
- By re-fitting the silicon parameters based on a large dataset, a new version, GFN1(Si)-xTB, shows better accuracy in predicting energies, nuclear forces, and geometries for silicon-containing systems.
Article 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 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.
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| http://dx.doi.org/10.1021/acs.jcim.1c01170 | DOI Listing |
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