SFMMoE: A Semi-Empirical Descriptor-Augmented Multi-Expert Graph Neural Network for Accurate Prediction of Singlet Fission Properties

Efficient identification of singlet fission (SF) candidates remains a significant challenge in the development of high-performance organic photovoltaic materials due to the high computational cost of accurately evaluating excited-state energetics. Here, we present SFMMoE, a graph neural network (GNN) that integrates a multiexpert multigating (MMoE) architecture with 2-HOP message passing. By integrating local topological information from molecular graphs with global molecular descriptors derived from semiempirical methods, SFMMoE enables simultaneous prediction of five key excited-state properties, including two thermodynamic criteria critical to SF: ΔE_gap1= ΔE_S1– 2ΔE_T1and ΔE_gap2= ΔE_T2– 2ΔE_T1. The model achieves a mean square error below 0.04 eV across all tasks, outperforming traditional machine learning and testing of GNN baselines. This work demonstrates that integrating multitask learning with expert specialization and graph–descriptor feature fusion substantially improves the prediction accuracy of excited-state energetics, enabling large-scale, low-cost virtual screening of SF materials with quantum-chemical accuracy. To facilitate broader access, a freely available and user-friendly online prediction server is provided at http://tech.iawnix.xyz/SFMMoE.