Efficient Screening of Organic Singlet Fission Molecules Using Graph Neural Networks.

Singlet fission (SF) provides a promising strategy for surpassing the Shockley-Queisser limit in photovoltaics, thereby enabling high-efficiency, sustainable solar energy harvesting. However, the identification of efficient SF materials is hindered by the limited availability of suitable molecular candidates and the high computational costs associated with conventional quantum-chemical methods for excited states. In this study, we introduce a high-throughput screening framework that integrates a graph neural network (GNN) with multi-level validation to accelerate the discovery of promising SF candidates. Trained on a previously reported FORMED database, the GNN yields highly accurate predictions for SF-relevant excited-state properties, demonstrating a mean absolute error of about 0.1 eV for S1, T1, and T2 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.