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Toward Prediction of Nonradiative Decay Pathways in Organic Compounds II: Two Internal Conversion Channels in BODIPYs

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posted on 05.02.2020, 15:07 authored by Zhou Lin, Alexander W. Kohn, Troy Van Voorhis
Boron-dipyrromethene (BODIPY) molecules are widely used as laser dyes and have therefore become a popular research topic within recent years. Numerous studies have been reported for the rational design of BODIPY derivatives based on their photophysical properties, including absorption and fluorescence wavelengths (λabs and λfl), oscillator strength (f), nonradiative pathways, and quantum yield (Φ). In the present work, we illustrate a theoretical, semiempirical model that accurately predicts Φ for various BODIPY compounds on the basis of inexpensive electronic structure calculations, following the data-driven algorithm proposed by us in a previous study [Kohn et al. J. Phys. Chem. C. 2019, 123, 15394]. The model allows us to identify the dominant nonradiative channel of any BODIPY molecule using its structure exclusively and to establish a correlation between the activation energy (Ea) and the fluorescence quantum yield (Φfl). On the basis of our calculations, either the S1 → S0 or LaLb internal conversion (IC) mechanism dominates in the majority of BODIPY derivatives, depending on the structural and electronic properties of the substituents. In either case, the nonradiative rate (knr) exhibits a straightforward Arrhenius-like relation with the associated Ea. More interestingly, the S1 → S0 mechanism proceeds via a highly distorted intermediate structure in which the core BODIPY plane and the substituent at the 1-position are twisted, while the internal rotation of the very same substituent induces the LaLb transition. Our model reproduces kfl, knr, and Φfl to mean absolute errors (MAEs) of 0.16 decades, 0.87 decades, and 0.26, when all outliers are considered. These results allow us to validate the predictive power of the proposed data-driven algorithm in Φfl. They also indicate that the model has a great potential to facilitate and accelerate the machine learning aided design of BODIPY dyes for imaging and sensing applications, given sufficient experimental data and appropriate molecular descriptors.

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