Generative design of singlet fission materials leveraging a fragment-oriented database.

ABSTRACT: Recently, we leveraged the FORMED repository made up of 116 687 synthesizeable molecules to deploy fragment-based high-throughput virtual screening (HTVS) and genetic algorithm (GA) searches of singlet fission (SF) molecular candidates. With these approaches, both prototypical (e.g., acenes, boron-dipyrromethane (BODIPY)) and unreported (e.g., heteroatom-rich mesoionic) classes of chromophore candidates fulfilling specific SF energetic requirements were identified. Yet, the reliance on predefined fragments limits chemical space exploration and, thus, the discovery of truly unforeseen molecular cores. Here, we exploit FORMED to train a generative learning framework driven by reinforcement learning and property predictions. The generative model rediscovers a diverse range of pr

SUBMITTER: Worakul T 

PROVIDER: S-EPMC12418038 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature