DOI

  • Andrey A Buglak
  • Asterios Charisiadis
  • Aimee Sheehan
  • Christopher J Kingsbury
  • Mathias O Senge
  • Mikhail A Filatov

Heavy-atom-free sensitizers forming long-living triplet excited states via the spin-orbit charge transfer intersystem crossing (SOCT-ISC) process have recently attracted attention due to their potential to replace costly transition metal complexes in photonic applications. The efficiency of SOCT-ISC in BODIPY donor-acceptor dyads, so far the most thoroughly investigated class of such sensitizers, can be finely tuned by structural modification. However, predicting the triplet state yields and reactive oxygen species (ROS) generation quantum yields for such compounds in a particular solvent is still very challenging due to a lack of established quantitative structure-property relationship (QSPR) models. In this work, the available data on singlet oxygen generation quantum yields (phi(Delta)) for a dataset containing >70 heavy-atom-free BODIPY in three different solvents (toluene, acetonitrile, and tetrahydrofuran) were analyzed. In order to build reliable QSPR model, a series of new BODIPYs were synthesized that bear different electron donating aryl groups in the meso position, their optical and structural properties were studied along with the solvent dependence of singlet oxygen generation, which confirmed the formation of triplet states via the SOCT-ISC mechanism. For the combined dataset of BODIPY structures, a total of more than 5000 quantum-chemical descriptors was calculated including quantum-chemical descriptors using density functional theory (DFT), namely M06-2X functional. QSPR models predicting phi Delta values were developed using multiple linear regression (MLR), which perform significantly better than other machine learning methods and show sufficient statistical parameters (R=0.88-0.91 and q(2)=0.62-0.69) for all three solvents. A small root mean squared error of 8.2 % was obtained for phi(Delta) values predicted using MLR model in toluene. As a result, we proved that QSPR and machine learning techniques can be useful for predicting phi Delta values in different media and virtual screening of new heavy-atom-free BODIPYs with improved photosensitizing ability.

Original languageEnglish
Pages (from-to)9934-9947
Number of pages14
JournalChemistry - A European Journal
Volume27
Issue number38
Early online date20 Apr 2021
DOIs
StatePublished - 7 Jul 2021

    Scopus subject areas

  • Catalysis
  • Organic Chemistry

    Research areas

  • BODIPY, machine learning, photosensitization, structure-property relationship, singlet oxygen, TRIPLET EXCITED-STATES, INTRAMOLECULAR CHARGE-TRANSFER, ACTIVITY-RELATIONSHIPS QSARS, PHOTODYNAMIC THERAPY, PHOTOREDOX CATALYSIS, ELECTRON-DONOR, PORPHYRIN, ACCEPTOR, DYES, DERIVATIVES, structure–property relationship

ID: 76250287