Efficient Screening of Organic Singlet Fission Molecules Using Graph Neural Networks

excitation energies. This capability facilitates the efficient screening of over 20 million molecular structures from both OE62 and QO2Mol databases. Our framework significantly reduces the computational demand associated with time-dependent density functional theory validation and identifies 180 potential SF molecules along with more than 1000 conformers. Subsequent assessments regarding synthetic accessibility, GW approximation and Bethe-Salpeter equation calculations further highlight a subset of experimentally feasible candidates among these SF candidates. The present approach exemplifies an effective, AI-driven strategy for accelerating the discovery of functional materials for sustainable optoelectronic application.