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Evaluation of a combination of local hybrid functionals with DFT-D3 corrections for the calculation of thermochemical and kinetic data

机译:评估局部混合功能与DFT-D3校正的组合以计算热化学和动力学数据

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Due to their position-dependent exact exchange admixture, local hybrid functionals offer a higher flexibility and thus the potential for more universal and accurate exchange correlation functionals compared to global hybrids with a constant admixture, as has been demonstrated in previous work. Yet, the local hybrid constructions used so far do not account for the inclusion of dispersion-type interactions. As a first exploratory step toward a more general approach that includes van der Waals-type interactions with local hybrids, the present work has added DFT-D3-type corrections to a number of simple local hybrid functionals. Optimization of only the s_8 and s_(r,6) parameters for the S22 set provides good results for weak interaction energies but deteriorates the excellent performance of the local hybrids for G3 atomization energies and for classical reaction barriers. A combined optimization of the two DFT-D3 parameters with one of the two parameters of the spin-polarized local mixing function (LMF) of a local hybrid for a more general optimization set provides simultaneously accurate dispersion energies, improved atomization energies, and accurate reaction barriers, as well as excellent alkane protobranching ratios. For other LMFs, the improvements of such a combined optimization for the S22 energies have been less satisfactory. The most notable advantage of the dispersion-corrected local hybrids over, for example, a B3LYP-D3 approach, is in the much more accurate reaction barriers.
机译:由于其位置相关的精确交换混合物,本地混合功能提供了更高的灵活性,因此与具有恒定混合物的全局混合相比,其具有更通用,更准确的交换相关功能的潜力。但是,到目前为止使用的本地混合结构并没有考虑到分散类型相互作用的包含。作为迈向包括范德华类型与局部混合体相互作用的更通用方法的第一步探索性工作,本工作为许多简单的局部混合体功能添加了DFT-D3型校正。仅优化S22集的s_8和s_(r,6)参数可为弱的相互作用能提供良好的结果,但会降低局部杂化体对G3雾化能和经典反应屏障的出色性能。将两个DFT-D3参数与局部混合动力系统的自旋极化局部混合函数(LMF)的两个参数之一组合在一起进行优化,以得到更通用的优化设置,可同时提供准确的分散能,改善的雾化能和准确的反应屏障,以及出色的烷烃原支化比。对于其他LMF,针对S22能量的这种组合优化的改进效果不太理想。相对于例如B3LYP-D3方法,分散校正的本地杂化体的最显着优势在于更为精确的反应障碍。

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