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AC/DC Analysis: Broad and Comprehensive Approach to Analyze Infrared Intensities at the Atomic Level

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Author(s):
Richter, Wagner E. ; Duarte, Leonardo J. ; Vidal, Luciano N. ; Bruns, Roy E.
Total Authors: 4
Document type: Journal article
Source: Journal of Physical Chemistry A; v. 125, n. 15, p. 11-pg., 2021-04-13.
Abstract

We present a complete theoretical protocol to partition infrared intensities into terms owing to individual atoms by two different but related approaches: the atomic contributions (ACs) show how the entire molecular vibrational motion affects the electronic structure of a single atom and the total infrared intensity. On the other hand, the dynamic contributions (DCs show how the displacement of a single atom alters the electronic structure of the entire molecule and the total intensity. The two analyses are complementary ways of partitioning the same total intensity and conserve most of the features of the total intensity itself. Combined, they are called the AC/DC analysis. These can be further partitioned following the CCTDP (or CCT) models according to the population analysis chosen by the researcher. The main conceptual features of the equations are highlighted, and representative numerical results are shown to support the interpretation of the equations. The results are invariant to rotation and translation and can readily be extended to molecules of any size, shape, or symmetry. Although the AC/DC analysis requires the choice of a charge model, all charge models that correctly reproduce the total molecular dipole moment can be used. A fully automated protocol managed by the Placzek program is made available, free of charge and with input examples. (AU)

FAPESP's process: 18/24844-7 - Using atomic polar tensors and QCT parameters to train a machine learning model and predict Hammett constants.
Grantee:Leonardo José Duarte
Support Opportunities: Scholarships abroad - Research Internship - 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