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Modeling continuous changes in substituent electronegativity and chemical hardness using fictitious nuclear potentials
Authors:Benjamin G. Janesko
Affiliation:1. Laboratoire d’électrochimie, Chimie des Interfaces et Modélisation pour l’énergie, UMR 7575, Chimie ParisTech, 11 rue P. et M. Curie, 75231, Paris Cedex 05, France
3. Institut de Sciences Analytiques, UMR 5280, Université Lyon 1; Université de Lyon, 43 boul. 11 novembre, 69622, Villeurbanne Cedex, France
2. Laboratoire Structure, Propriété et Modélisation des Solides, UMR 8580, école Centrale Paris, Grande Voie des Vignes, 92295, Chatenay-Malabry, France
Abstract:The performances of a family of recently developed generalized gradient approximation (GGA) functionals based on the Tognetti–Cortona–Adamo (TCA) family and making use of the gradient-regulated connection (GRAC) approach are here tested on an uncommon benchmark set for the prediction of transition state (TS) structures and energies of a series of four reactions involving an early transition metal (Zr, d 0). This benchmark test thus represents the first step in the organometallic world in which d n ions allowing complex phenomena such as spin crossover represent the higher level of complexity. The results obtained show that the performances of the GRAC-xxx functionals are comparable to those of global hybrid functionals both in the prediction of reaction barriers and of structural features of TSs. More complex functional forms (such as range-separated hybrids) in average enhance the energetic features, but not necessarily the overall accuracy on calculated structures. On the other hand, and as expected, purposely developed functionals for the prediction of chemical reactivity provide both structural and energetic features in good agreement with post-HF results. The present study, besides proving the good performances of GGA functionals of the GRAC-TCA family for the prediction of TS structural parameters and energetics of metal containing systems, also underlines the importance of the use of diversified benchmark sets to allow a fair evaluation of functionals performances.
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