Center for Molecular Modeling (CMM), Ghent University, Technologiepark-Zwijnaarde 46, B-9052 Ghent, Belgium.
J Chem Inf Model. 2021 Dec 27;61(12):5931-5937. doi: 10.1021/acs.jcim.1c01170. Epub 2021 Dec 10.
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.
一种通用的密度泛函紧束缚方法，即 GFN-xTB 模型，在超出传统形式体系范围的精确模拟中越来越受欢迎。我们表明，在其原始的 GFN1-xTB 参数化中，有机硅化合物的描述很差。通过将模型的硅参数重新拟合到由 revPBE 泛函优化的 10000 个参考化合物的数据集来解决这个问题。得到的 GFN1（Si）-xTB 参数化在预测体系能量、核力和几何形状方面具有更高的准确性，对于包含硅的 GFN-xTB 哈密顿量应用于所有系统的应用都应考虑使用。
