Singlet fission (SF) is the conversion of one photo-generated singlet-state exciton into two tripletstate excitons. SF has the potential to significantly enhance the efficiency of solar cells by harvesting two charge carriers from one photon. Molecular crystals that undergo SF in the solid state are scarce. Computational exploration of the chemical space may accelerate the discovery of new SF materials.However, many-body perturbation theory (MBPT) methods that can describe the excitonic properties of molecular crystals are computationally too expensive for large-scale materials screening. We use the sure-independence-screening-and-sparsifying-operator (SISSO) machine-learning algorithm to generate models that are fast to evaluate and can predict the thermodynamic driving force for SF, calculated by MBPT for a dataset of 101 polycyclic aromatic hydrocarbons (PAH101). SISSO generates models by iteratively combining physically motivated primary features. The best performing models are then selected by linear regression with cross validation. The SISSO-generated models successfully predict the SF driving force with root mean square errors below 0.2 eV. Based on considerations of the cost, accuracy, and classification performance of SISSO-generated models, we propose a hierarchical screening workflow for materials discovery. Three new SF candidates are discovered in the PAH101 set, which belong to chemical families not previously explored in the context of SF.

Finding predictive models for singlet fission by machine learning

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