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Conference Papers Year : 2021

Unsupervised topological learning approach of crystal nucleation in pure Tantalum

Abstract

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 unraveled. Crystal nucleation, the early stages where the liquid-to-solid transition occurs upon undercooling, initiates at the atomic level on nanometer 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 a monatomic metal, namely Tantalum (Ta), it shows that both translational and orientational ordering always come into play simultaneously when homogeneous nucleation starts in regions with low five-fold symmetry.
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Dates and versions

hal-03806275 , version 1 (11-10-2022)

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Sébastien Becker, Emilie Devijver, Rémi Molinier, Noël Jakse. Unsupervised topological learning approach of crystal nucleation in pure Tantalum. Fourth Workshop on Machine Learning and the Physical Sciences (NeurIPS 2021), Dec 2021, Vancouver, Canada. ⟨hal-03806275⟩
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