From photosensitizers to light harvesters adapting the molecular structure in all-BODIPY assemblies

Dyads and Triads of Boron Difluoride Formazanate and Boron Difluoride Dipyrromethene Dyes

Dye-dye conjugates have attracted significant interest for their utility in applications such as bioimaging, theranostics, and light-harvesting. Many classes of organic dyes have been employed in this regard; however, building blocks don't typically extend beyond small chromophores. This can lead to minor changes to the optoelectronic properties of the original dye. The exploration of dye-dye structures is impeded by long synthetic routes, incompatible synthetic conditions, or a mismatch of the desired properties. Here, we present the first-of-their-kind dye-dye conjugates of boron difluoride complexes of formazanate and dipyrromethene ligands. These conjugates exhibit dual photoluminescence bands that reach the near-infrared spectral region and implicate anti-Kasha processes. Cyclic voltammetry experiments revealed the generation of polyanionic species that can reversibly tolerate the uptake of up to 6 electrons. Ultimately, we demonstrate that BF2 formazanates can serve as a synthetically accessible platform to build upon new classes of dye-dye conjugates.

Data-driven modeling of S0 → S1 excitation energy in the BODIPY chemical space: High-throughput computation, quantum machine learning, and inverse design

Derivatives of BODIPY are popular fluorophores due to their synthetic feasibility, structural rigidity, high quantum yield, and tunable spectroscopic properties. While the characteristic absorption maximum of BODIPY is at 2.5 eV, combinations of functional groups and substitution sites can shift the peak position by ±1 eV. Time-dependent long-range corrected hybrid density functional methods can model the lowest excitation energies offering a semi-quantitative precision of ±0.3 eV. Alas, the chemical space of BODIPYs stemming from combinatorial introduction of-even a few dozen-substituents is too large for brute-force high-throughput modeling. To navigate this vast space, we select 77 412 molecules and train a kernel-based quantum machine learning model providing <2% hold-out error. Further reuse of the results presented here to navigate the entire BODIPY universe comprising over 253 giga (253 × 10⁹) molecules is demonstrated by inverse-designing candidates with desired target excitation energies.