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.