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

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Autor(es):
Richter, Wagner E. ; Duarte, Leonardo J. ; Vidal, Luciano N. ; Bruns, Roy E.
Número total de Autores: 4
Tipo de documento: Artigo Científico
Fonte: Journal of Physical Chemistry A; v. 125, n. 15, p. 11-pg., 2021-04-13.
Resumo

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)

Processo FAPESP: 18/24844-7 - Uso de tensores polares atômicos e parâmetros QCT para treinar um modelo de machine learning e prever constantes de Hammett
Beneficiário:Leonardo José Duarte
Modalidade de apoio: Bolsas no Exterior - Estágio de Pesquisa - Doutorado Direto
Processo FAPESP: 18/08861-9 - Aplicação do modelo QTAIM / CCTDP e machine learning para a previsão de reatividades químicas
Beneficiário:Roy Edward Bruns
Modalidade de apoio: Auxílio à Pesquisa - Regular