Unsupervised topological learning approach of crystal nucleation

Sébastien Becker (1, 2) , Emilie Devijver (2) , Rémi Molinier (3) , Noël Jakse (1)

1 SIMaP - Science et Ingénierie des Matériaux et Procédés
2 LIG - Laboratoire d'Informatique de Grenoble
3 IF - Institut Fourier

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 where 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.

Domaines: Physique [physics]

Sébastien Becker, Emilie Devijver, Rémi Molinier, Noël Jakse. Unsupervised topological learning approach of crystal nucleation. Scientific Reports, 2022, ⟨10.1038/s41598-022-06963-5⟩. ⟨hal-03806284⟩