Unsupervised topological learning approach of crystal nucleation in pure Tantalum

TL;DR

This paper introduces an unsupervised topological learning method using persistent homology to analyze atomic-scale crystal nucleation in Tantalum, revealing simultaneous translational and orientational ordering during nucleation.

Contribution

It presents a novel unsupervised approach based on topological descriptors to study complex nucleation mechanisms without prior assumptions.

Findings

Nucleation involves both translational and orientational order.

Low five-fold symmetry regions are critical in nucleation.

Method successfully captures atomic-level structural features.

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.