Multistep nucleation of anisotropic molecules

Multistep nucleation phenomena are of considerable fundamental interest. Here the authors combine molecular dynamics, machine learning and molecular cluster analysis to investigate the multistep nucleation of smectic clusters from a nematic fluid that cannot be accounted for by the classical nucleation theory. Phase transition of anisotropic materials is ubiquitously observed in physics, biology, materials science, and engineering. Nevertheless, how anisotropy of constituent molecules affects the phase transition dynamics is still poorly understood. Here we investigate numerically the phase transition of a simple model system composed of anisotropic molecules, and report on our discovery of multistep nucleation of nuclei with layered positional ordering (smectic ordering), from a fluid-like nematic phase with orientational order only (no positional order). A trinity of molecular dynamics simulation, machine learning, and molecular cluster analysis yielding free energy landscapes unambiguously demonstrates the dynamics of multistep nucleation process involving characteristic metastable clusters that precede supercritical smectic nuclei and cannot be accounted for by the classical nucleation theory. Our work suggests that molecules of simple shape can exhibit rich and complex nucleation processes, and our numerical approach will provide deeper understanding of phase transitions and resulting structures in anisotropic materials such as biological systems and functional materials.

Automated summary

Researchers used molecular dynamics, machine learning, and molecular cluster analysis to investigate the multistep nucleation of smectic clusters not accounted for by classical theory. They observed characteristic metastable clusters preceding supercritical nuclei, revealing the impact of molecular shape on phase transition dynamics and providing deeper understanding of anisotropic materials.