We propose an unsupervised learning methodology build on a Gaussian mixture model computed on topological descriptors from persistent homology, for the structural analysis of materials at the atomic scale. Based only on atomic positions and without t
extit{a priori} knowledge, our method automatically identifies relevant local atomic structures in a system of interest. Along with a complete description of the procedure, we provide a concrete example of application by analysing large-scale molecular dynamics simulations of bulks crystal and liquid as well as homogeneous nucleation events of elemental Zr at the nose of the time-temperature-transformation curve. This method opens the way to deeper and autonomous studies of structural dependent phenomena occurring at the atomic scale in materials.
Nucleation phenomena commonly observed in our every day life are of fundamental, technological and societal importance in many areas, but some of their most intimate mechanisms remain however to be unravelled. Crystal nucleation, the early stages whe
re the liquid-to-solid transition occurs upon undercooling, initiates at the atomic level on nanometre length and sub-picoseconds time scales and involves complex multidimensional mechanisms with local symmetry breaking that can hardly be observed experimentally in the very details. To reveal their structural features in simulations without a priori, an unsupervised learning approach founded on topological descriptors loaned from persistent homology concepts is proposed. Applied here to monatomic metals, it shows that both translational and orientational ordering always come into play simultaneously when homogeneous nucleation starts in regions with low five-fold symmetry. It also reveals the specificity of the nucleation pathways depending on the element considered, with features beyond the hypothesis of Classical Nucleation Theory.
