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Improved Infrared Spectra Prediction by DFT from a New Experimental Database.
Katari, Madanakrishna; Nicol, Edith; Steinmetz, Vincent; van der Rest, Guillaume; Carmichael, Duncan; Frison, Gilles.
Affiliation
  • Katari M; LCM, CNRS, Ecole Polytechnique, Université Paris-Saclay, 91128, Palaiseau, France.
  • Nicol E; LCM, CNRS, Ecole Polytechnique, Université Paris-Saclay, 91128, Palaiseau, France.
  • Steinmetz V; Laboratoire de Chimie Physique, Université Paris Sud, CNRS, 91405, Orsay, France.
  • van der Rest G; Laboratoire de Chimie Physique, Université Paris Sud, CNRS, 91405, Orsay, France.
  • Carmichael D; LCM, CNRS, Ecole Polytechnique, Université Paris-Saclay, 91128, Palaiseau, France.
  • Frison G; LCM, CNRS, Ecole Polytechnique, Université Paris-Saclay, 91128, Palaiseau, France.
Chemistry ; 23(35): 8414-8423, 2017 Jun 22.
Article de En | MEDLINE | ID: mdl-28295724
ABSTRACT
This work aims to improve the computation of infrared spectra of gas-phase cations using DFT methods. Experimental infrared multiple photon dissociation (IRMPD) spectra for ten Zn and Ru organometallic complexes have been used to provide reference data for 64 vibrational modes in the 900-2000 cm-1 range. The accuracy of the IR vibrational frequencies predicted for these bands has been assessed over five DFT functionals and three basis sets. The functionals include the popular B3LYP and M06-2X hybrids and the range-separated hybrids (RSH) CAM-B3LYP, LC-BLYP, and ωB97X-D. B3LYP gives the best mean absolute error (MAE) and root-mean-square error (RMSE) values of 7.1 and 9.6 cm-1 , whilst the best RSH functional, ωB97X-D, gives 12.8 and 16.6 cm-1 , respectively. Using linear correlations instead of scaling factors improves the prediction accuracy significantly for all functionals. Experimental and computed spectra for a single complex can show significant differences even when the molecular structure is calculated correctly, and a means of defining confidence limits for any given computed structure is also provided.
Mots clés

Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Type d'étude: Prognostic_studies / Risk_factors_studies Langue: En Journal: Chemistry Sujet du journal: QUIMICA Année: 2017 Type de document: Article Pays d'affiliation: France

Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Type d'étude: Prognostic_studies / Risk_factors_studies Langue: En Journal: Chemistry Sujet du journal: QUIMICA Année: 2017 Type de document: Article Pays d'affiliation: France