Modeling the Nonradiative Decay of Cyclooctatetrathiophene in Solution and Crystal Phases Using Machine Learning

Modeling excited-state dynamics of molecular aggregates via multiscale calculations remains challenging due to the resource-intensive excited-state electronic structure calculations. This study presents a direct combination of machine learning (ML)-accelerated excited-state calculations and semiempirical-level molecular mechanics methods, specifically GFN-FF, within the ONIOM scheme. The ML-photodynamics simulations reveal the critical role of neighboring πCC and σCC bond torsion in controlling the nonradiative decay of the well-known aggregation-induced emission (AIE) molecule cyclooctatetrathiophene (COTh) in THF solution and COTh crystal. The predicted fluorescence quantum yield enhancement factors, ranging from 3- to 22-fold in the solution to the crystal, are in good agreement with the experimental results. The trajectory analysis revealed increasing restrictions on the πCC bond torsion of COTh from the THF solution to the COTh crystal, which blocked the nonradiative decay pathways. Our approach provides a quantitative understanding of the AIE mechanisms of COTh and is expected to be applied in the rational design of AIE materials in the future.