Unknown

Dataset Information

0

Geminal-Based Strategies for Modeling Large Building Blocks of Organic Electronic Materials.


ABSTRACT: We elaborate on unconventional electronic structure methods based on geminals and their potential to advance the rapidly developing field of organic photovoltaics (OPVs). Specifically, we focus on the computational advantages of geminal-based methods over standard approaches and identify the critical aspects of OPV development. Examples are reliable and efficient computations of orbital energies, electronic spectra, and van der Waals interactions. Geminal-based models can also be combined with quantum embedding techniques and a quantum information analysis of orbital interactions to gain a fundamental understanding of the electronic structures and properties of realistic OPV building blocks. Furthermore, other organic components present in, for instance, dye-sensitized solar cells (DSSCs) represent another promising scope of application. Finally, we provide numerical examples predicting the properties of a small building block of OPV components and two carbazole-based dyes proposed as possible DSSC sensitizers.

SUBMITTER: Tecmer P 

PROVIDER: S-EPMC10641881 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature

altmetric image

Publications

Geminal-Based Strategies for Modeling Large Building Blocks of Organic Electronic Materials.

Tecmer Paweł P   Gałyńska Marta M   Szczuczko Lena L   Boguslawski Katharina K  

The journal of physical chemistry letters 20231030 44


We elaborate on unconventional electronic structure methods based on geminals and their potential to advance the rapidly developing field of organic photovoltaics (OPVs). Specifically, we focus on the computational advantages of geminal-based methods over standard approaches and identify the critical aspects of OPV development. Examples are reliable and efficient computations of orbital energies, electronic spectra, and van der Waals interactions. Geminal-based models can also be combined with q  ...[more]

Similar Datasets

| S-EPMC9075834 | biostudies-literature
| S-EPMC6491992 | biostudies-literature
| S-EPMC8456814 | biostudies-literature
| S-EPMC5355808 | biostudies-literature
| S-EPMC7173703 | biostudies-literature
| S-EPMC8156644 | biostudies-literature
| S-EPMC9222365 | biostudies-literature
| S-EPMC7699513 | biostudies-literature
| S-EPMC7924717 | biostudies-literature
| S-EPMC9044473 | biostudies-literature