Exploration of\nFusion and Fission of Molecular Assemblies\nin Aqueous Solution through a Dynamic Clustering Approach

To understand the mechanism of self-assembly and to predict\nthe\nevolutionary pattern of the fusion–fission system over a long\nperiod of time, studying the dynamics of these processes is of great\nsignificance. The trajectories from molecular dynamics (MD) simulations\nof self-assembly processes contain numerous latent fusion and fission\nevents. To analyze the fusion and fission events from the simulated\ntrajectory, in this article, a dynamic clustering approach was developed\nby comparing the changes of monomer composition within clusters over\nsimulated time. The rates of fusion and fission events obtained from\ndynamic clustering analysis were further coupled with the population\nbalance model (PBM), and the evolution of the molecular assemblies\ncalculated is reasonably consistent with the corresponding MD simulation\nresults. This approach provides a new idea to analyze the big data\nof molecular self-assembly dynamics obtained from MD simulations,\nand it offers a computationally achievable approach to connect discrete\nevents at the molecular scale with continuum equations such as the\nPBM at the macroscale.