Machine Learning Models for Predicting Electronic Coupling in TEMPO/TEMPO+ Systems

Organic Radical Batteries (ORBs) based on the TEMPO (2,2,6,6-tetramethylpiperidin-1-yl oxyl) radical have drawn significant attention owing to their unique redox properties. A key factor influencing ORB's redox properties, i.e., the kinetic of the electron transfer between the TEMPO-TEMPO+ pairs is the communication between the underlying redox-active states as given by the electronic coupling. However, due to the complex structure, predicting accurate electronic couplings for these pairs is computationally expensive and challenging. In this study, we introduce a machine learning (ML) workflow to predict the electronic coupling for TEMPO-TEMPO+ pairs simply from their specific geometric orientations. For the ML models, a dataset was generated through time-dependent density functional theory calculations coupled with the Generalized Mulliken Hush method to assess energies, (transition-)dipole moment and couplings for specific TEMPO-TEMPO+ configurations obtained from classical molecular dynamics simulations that mimic a realistic electrolyte environment. Our results demonstrate that, among the three ML models—linear regression, kernel ridge regression (KRR), and random forest—the KRR model, with its kernel-based approach, most effectively handles the correlated orientation-based descriptors. Moreover, our SHapley Additive exPlanations (SHAP)-based feature importance analysis indicates that multiple orientation factors jointly influence electronic the coupling, rather than any single distance or angle dominating, with each parameter’s impact strongly contingent on the values of the others which is in agreement with previous studies computational by the consortium.