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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

navoidable failure of point charge descriptions of electronic density changes for out-of-plane distortion

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Author(s):
Richter, Wagner E. [1] ; Duarte, Leonardo J. [2] ; Bruns, Roy E. [2]
Total Authors: 3
Affiliation:
[1] Univ Tecnol Fed Parana, Dept Chem Engn, BR-84017220 Ponta Grossa, Parana - Brazil
[2] Univ Estadual Campinas, Inst Chem, POB 6154, BR-13083970 Campinas, SP - Brazil
Total Affiliations: 2
Document type: Journal article
Source: SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY; v. 271, APR 15 2022.
Web of Science Citations: 0
Abstract

Population analyses based on point charge approximations accurately estimating the equilibrium dipole moment will systematically fail when predicting infrared intensities of out-of-plane vibrations of planar molecules, whereas models based on both charges and dipoles will always succeed. It is not a matter of how the model is devised but rather how many degrees of freedom are available for the calculation. Population analyses based on point charges are very limited in terms of the amount of meaningful chemical information they provide, whereas models employing both atomic charges and atomic dipoles should be preferred for molecular distortions. A good model should be able to correctly describe not only static, equilibrium structures but also distorted geometries in order to correctly assess information from vibrating molecules. The limitations of point charge models also hold for distortions much larger than those encountered vibrationally. (C) 2022 Elsevier B.V. All rights reserved. (AU)

FAPESP's process: 17/22741-3 - Using atomic multipoles and developing machine learning models to investigate transition states
Grantee:Leonardo José Duarte
Support Opportunities: Scholarships in Brazil - Doctorate (Direct)
FAPESP's process: 18/08861-9 - Application of the QTAIM / CCTDP model and machine learning for the forecast of chemical reactivities
Grantee:Roy Edward Bruns
Support Opportunities: Regular Research Grants