Kinetic network models to elucidate aggregation dynamics of aggregation‐induced emission systems

Abstract

Abstract Aggregation‐induced emission (AIE) is a phenomenon where a molecule that is weakly or non‐luminescent in a diluted solution becomes highly emissive when aggregated. AIE luminogens (AIEgens) hold promise in diverse applications like bioimaging, chemical sensing, and optoelectronics. Investigation in AIE luminescence is also critical for understanding aggregation kinetics as the aggregation process is an essential component of AIE emission. Experimental investigation of AIEgen aggregation is challenging due to the fast timescale of the aggregation and the amorphous aggregate structures. Computer simulations such as molecular dynamics (MD) simulation provide a valuable approach to complement experiments with atomic‐level knowledge to study the fast dynamics of aggregation processes. However, individual simulations still struggle to systematically elucidate heterogeneous kinetics of the formation of amorphous AIEgen aggregates. Kinetic network models (KNMs), constructed from an ensemble of MD simulations, hold great potential in addressing this challenge. In these models, dynamic processes are modeled as a series of Markovian transitions occurring among metastable conformational states at discrete time intervals. In this perspective article, we first review previous studies to characterize the AIEgen aggregation kinetics and their limitations. We then introduce KNMs as a promising approach to elucidate the complex kinetics of aggregations to address these limitations. More importantly, we discuss our perspective on linking the output of KNMs to experimental observations of time‐resolved AIE luminescence. We expect that this approach can validate the computational predictions and provide great insights into the aggregation kinetics for AIEgen aggregates. These insights will facilitate the rational design of improved AIEgens in their applications in biology and materials sciences.

Keywords

• hydrophobic aggregation
• kinetic network models
• molecular dynamics simulations