Unsupervised topological learning for identification of atomic structures - Laboratoire d'Informatique de Grenoble Accéder directement au contenu
Article Dans Une Revue Physical Review E Année : 2022

Unsupervised topological learning for identification of atomic structures

Résumé

We propose an unsupervised learning methodology with descriptors based on Topological Data Analysis (TDA) concepts to describe the local structural properties of materials at the atomic scale. Based only on atomic positions and without a priori knowledge, our method allows for an autonomous identification of clusters of atomic structures through a Gaussian mixture model. We apply successfully this approach to the analysis of elemental Zr in the crystalline and liquid states as well as homogeneous nucleation events under deep undercooling conditions. This opens the way to deeper and autonomous study of complex phenomena in materials at the atomic scale.
Fichier principal
Vignette du fichier
unsupervised_topological_learning_ArXiV.pdf (24.14 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03806268 , version 1 (07-10-2022)

Identifiants

Citer

Sébastien Becker, Emilie Devijver, Rémi Molinier, Noël Jakse. Unsupervised topological learning for identification of atomic structures. Physical Review E , 2022, ⟨10.1103/PhysRevE.105.045304⟩. ⟨hal-03806268⟩
40 Consultations
3 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More