  Understanding the mechanisms underlying crystal formation is crucial. For
most systems, crystallization typically goes through a nucleation process that
involves dynamics that happen at short time and length scales. Due to this,
molecular dynamics serves as a powerful tool to study this phenomenon. Existing
approaches to study the mechanism often focus analysis on static snapshots of
the global configuration, potentially overlooking subtle local fluctuations and
history of the atoms involved in the formation of solid nuclei. To address this
limitation, we propose a methodology that categorizes nucleation pathways into
reactive pathways based on the time evolution of constituent atoms. Our
approach effectively captures the diverse structural pathways explored by
crystallizing Lennard-Jones-like particles and solidifying Ni$_3$Al, providing
a more nuanced understanding of nucleating pathways. Moreover, our methodology
enables the prediction of the resulting polymorph from each reactive
trajectory. This deep learning-assisted comprehensive analysis offers an
alternative view of crystal nucleation mechanisms and pathways.
