Alternative CNDOL Fockians for fast and accurate description of molecular exciton properties

Author:

Montero-Cabrera Luis A.12ORCID,Montero-Alejo Ana L.3ORCID,Aspuru-Guzik Alan4ORCID,García de la Vega José M.5ORCID,Piris Mario2ORCID,Díaz-Fernández Lourdes A.1,Pérez-Badell Yoana1ORCID,Guerra-Barroso Alberto1,Alfonso-Ramos Javier E.1ORCID,Rodríguez Javier1,Fuentes María E.6ORCID,de Armas Carlos M.1

Affiliation:

1. Laboratorio de Química Computacional y Teórica, Departamento de Química Física, Universidad de La Habana 1 , 10400 Havana, Cuba

2. Donostia International Physics Center (DIPC) 2 , 20018 Donostia – San Sebastián, Basque Country, Spain

3. Departamento de Física, Facultad de Ciencias Naturales, Matemática y del Medio Ambiente (FCNMM), Universidad Tecnológica Metropolitana; Ñuñoa 3 , Santiago 7800002, Chile

4. Department of Chemistry, University of Toronto 4 , Toronto, Ontario M5S 3H6, Canada

5. Departamento de Química Física Aplicada, Universidad Autónoma de Madrid 5 , 28049 Madrid, Spain

6. Facultad de Ciencias Químicas, Universidad Autónoma de Chihuahua 6 , Chihuahua, 31100 Chihuahua, Mexico

Abstract

CNDOL is an a priori, approximate Fockian for molecular wave functions. In this study, we employ several modes of singly excited configuration interaction (CIS) to model molecular excitation properties by using four combinations of the one electron operator terms. Those options are compared to the experimental and theoretical data for a carefully selected set of molecules. The resulting excitons are represented by CIS wave functions that encompass all valence electrons in the system for each excited state energy. The Coulomb–exchange term associated to the calculated excitation energies is rationalized to evaluate theoretical exciton binding energies. This property is shown to be useful for discriminating the charge donation ability of molecular and supermolecular systems. Multielectronic 3D maps of exciton formal charges are showcased, demonstrating the applicability of these approximate wave functions for modeling properties of large molecules and clusters at nanoscales. This modeling proves useful in designing molecular photovoltaic devices. Our methodology holds potential applications in systematic evaluations of such systems and the development of fundamental artificial intelligence databases for predicting related properties.

Funder

Agencia Española de Cooperación Internacional para el Desarrollo

Idaho Operations Office, U.S. Department of Energy

Ministerio de Ciencias, Tecnología y Medio Ambiente

Publisher

AIP Publishing

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