Navigating Sparse Singlet Fission Chemical Space: An Intelligent Generative-Predictive Paradigm
Singlet fission (SF) offers a promising route to surpass the Shockley-Queisser limit by converting a photoexcited singlet exciton into two triplet excitons, thereby enhancing photovoltaic energy conversion efficiency. However, realizing efficient SF process requires stringent energetic requirements among low-lying excited states that render SF molecules intrinsically rare within the vast chemical space. This extreme sparsity presents a grand challenge for molecular discovery. Due to low hit rates and trial-and-error computational waste on nonviable structures, conventional high-throughput virtual screening faces significant constraints, even when accelerated by machine learning models. Here, we establish a synergistic generative-predictive framework for the targeted inverse design of SF molecules by integrating a structure generator, a properties predictor and a multi-criteria validation workflow. By continuously coupling generative exploration with SF predictive models, the framework progressively enriches SF species and achieves a success rate of approximately 90% in generating molecules that satisfy the target SF energetic criteria. High-throughput evaluation of about 100 million generated structures with time-dependent density functional theory (TDDFT) validation of just a random 1% subset confirmed a 90.8% success rate for SF candidates. All together, we constructed an SF database of 283,559 candidates with favorable energetics of excited states and synthetic accessibility. From it, we identified a key fragment strongly associated with the requirements for SF, namely, CN([O])N(C)[O]. These findings establish an efficient route for overcoming the sparsity difficulty in SF molecular discovery and provide interpretable design principles for the development of novel excited-state functional materials.