Artificial Intelligence-Driven Inverse Design of Singlet Fission Candidates in the Acene family.

In this work, we describe the successful application of an AI-driven inverse design protocol to identify singlet fission (SF) candidates within the acene family, exploring a chemical space of ≈1019 molecules. Substituent effects are encoded via Hammett σ constants, providing a chemically interpretable and generalizable descriptor for unseen functional groups. A Gated Recurrent Unit (GRU)-based Recurrent Neural Network (RNN) model is used to accurately predict excited singlet (S1) and triplet (T1) states energies. The model is coupled with optimization algorithms, including Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), to efficiently navigate chemical space, optimize S1 and T1 energies, and reveal recurring substituent patterns. This approach not only identifies well-known tetracene and pentacene candidates but also uncovers viable benzene, naphthalene, and anthracene derivatives that satisfy SF criteria, which are typically inaccessible via intuition. The methodology, accessible at https://alba.ugr.es/acene/, establishes a versatile and interpretable platform for rational molecular design, enabling the exploration of large chemical spaces and the discovery of compounds with tailored excited-state properties.