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: ΔEgap1 =
ΔES1 – 2ΔET1 and ΔEgap2 = ΔET2 – 2ΔET1. 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.