Abstract:
Aggregation-induced emission (AIE) is a photophysical phenomenon in which weakly luminescent organic chromophores become strongly luminescent in aggregate. The reduced non-radiative decay in aggregates is often cited as the explanation of AIE. However, the mechanism of competing non-radiative decay pathways is not resolved due to the lack of excited-state structural information in the time-resolved experiments and prohibitively expensive quantum mechanical calculations for photodynamics simulations. We investigated the excited-state dynamics of classic AIE molecules in aggregate, hexaphenylsilole (HPS), tetraphenylsilole (TPS), and cyclooctatetrathiophene (COTh) with a multiscale machine learning accelerated photodynamics approach, integrating neural networks, semiempirical methods, and molecular mechanics. Our simulations predict 263, 5, and 12-fold fluorescence enhancement of HPS, TPS, and COTh in good agreement with the experiments (255, 3, and 12). We identified a shared non-radiative decay mechanism involving πCC torsions in these molecules. These torsions are blocked in aggregate due to intermolecular hindrance between substituents, promoting AIE.
Biography:
Jingbai Li received his Ph.D. from the Illinois Institute of Technology in 2019. He then worked with Professor Steven A. Lopez at Northeastern University as a postdoctoral researcher in 2019–2022, where he developed the machine learning (ML) photodynamics approach for PyRAI2MD. He joined the Hoffmann Institute of Advanced Materials at Shenzhen Polytechnic University in 2022. His lab is focuses on developing and applying the ML photodynamics approach for simulating complex chemical systems in solution, aggregate, and solid state.
Seminar Series by the NYU-ECNU Center for Computational Chemistry at NYU Shanghai
This event is open to the NYU Shanghai, NYU, ECNU community and the computational chemistry community.