Abstract
The study of photoexcitations in molecular aggregates faces the twofold problem of the increased computational cost associated with excited states and the complexity of the interactions among the constituent monomers. A mechanistic investigation of these processes requires the analysis of the intermolecular interactions, the effect of the environment, and 3D arrangements or crystal packing on the excited states. A considerable number of techniques have been tailored to navigate these obstacles; however, they are usually restricted to in‐house codes and thus require a disproportionate effort to adopt by researchers approaching the field. Herein, we present the FRamewOrk for Molecular AGgregate Excitations (fromage), which implements a collection of such techniques in a Python library complemented with ready‐to‐use scripts. The program structure is presented and the principal features available to the user are described: geometrical analysis, exciton characterization, and a variety of ONIOM schemes. Each is illustrated by examples of diverse organic molecules in condensed phase settings. The program is available at https://github.com/Crespo-Otero-group/fromage.
Highlights
The computational study of photochemistry in molecular condensed phases represents a notoriously difficult problem
We offer the FRamewOrk for Molecular AGgregate Excitations. fromage is a standalone Python library, accompanied by ready-to-use command line scripts destined to facilitate the study of molecular aggregates in the excited state
The S1 and S2 states are separated by twice the magnitude of the exciton coupling, provided that the individual constituent molecules are in perfect resonance.[37]
Summary
The computational study of photochemistry in molecular condensed phases represents a notoriously difficult problem. Fromage is a standalone Python library, accompanied by ready-to-use command line scripts destined to facilitate the study of molecular aggregates in the excited state Armed with the definition of a bond, the Mol class can single out covalently bonded complexes from an aggregate, generate molecular clusters from a single crystal, and detect atomic connectivity These tools are in and of themselves useful as a library for the Python literate user. To illustrate the use of this feature, a dimer of perylene was extracted from its experimental crystal structure.[34] Its excited states were calculated in Gaussian using TD-ωB97X-D/ 6-31G(d), and the transition densities analyzed using the orbital specific Mulliken partition scheme.
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