Improved Infrared Spectra Prediction by DFT from a New Experimental Database.
Madanakrishna KatariEdith NicolVincent SteinmetzGuillaume van der RestDuncan CarmichaelGilles FrisonPublished in: Chemistry (Weinheim an der Bergstrasse, Germany) (2017)
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.
Keyphrases
- density functional theory
- molecular dynamics
- single molecule
- magnetic resonance imaging
- computed tomography
- simultaneous determination
- mass spectrometry
- big data
- molecular docking
- deep learning
- artificial intelligence
- energy transfer
- molecular dynamics simulations
- high resolution
- liquid chromatography
- contrast enhanced
- fluorescent probe