Size-transferable prediction of excited state properties for molecular assemblies with machine-learned exciton model

Computational modeling of the excited states of molecular aggregates faces significant computational challenges and size heterogeneity. Current machine learning (ML) models, typically trained on specific-sized aggregates, struggle with scalability. We found that the exciton model Hamiltonian of large aggregates can be decomposed into dimer pairs, allowing an ML model trained on dimers to reconstruct Hamiltonians for aggregates of any size. We trained an ML model on perylene dimer exciton Hamiltonians using quantum chemistry descriptors to resolve phase correction issues. Our model accurately predicted excitation energies and oscillator strengths in perylene trimers and tetramers, and analyzed the size-dependent optical bandgap in aggregates with up to 50 monomers, revealing quantum dot-like confinement effects. Future work will explore transferability across different monomers to predict optical properties in heterogeneous assemblies.