Effective generation of heavy-atom-free triplet photosensitizers containing multiple intersystem crossing mechanisms based on deep learning.

ABSTRACT: Photodynamic therapy (PDT) is a clinically approved therapeutic modality that has demonstrated significant potential for cancer treatment, and triplet photosensitizers (PSs) play a key role in its efficacy. Despite deep learning having emerged as a next-generation tool for material discovery, existing methods mainly target a limited subset of triplet PSs, such as thermally activated delayed fluorescence (TADF) materials, neglecting the critical intersystem crossing (ISC) between the high-lying singlet and triplet states (ΔE _{S n T n} ). To overcome this limitation, we compiled a comprehensive dataset (∼1.90 × 10⁹) of triplet PSs encompassing various ISC mechanisms. Then, we proposed a novel strategy that incorporates two model

SUBMITTER: Chen K 

PROVIDER: S-EPMC12261949 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature