Pré-Publication, Document De Travail (Preprint/Prepublication) Année : 2024

All-around local structure classification with supervised learning: The example of crystal phases and dislocations in complex oxides

Résumé

To accurately identify local structures in atomic-scale simulations of complex materials is crucial for the study of numerous physical phenomena including dynamic plasticity, crystal nucleation and glass formation. In this work, we propose a data-driven method to characterize local atomic environments, and assign them to crystal phases or lattice defects. After constructing a reference database, our approach uses descriptors based on Steinhardt's parameters and a Gaussian mixture model to identify the most probable environment. This approach is validated against several test cases : polymorph identification in alumina, and dislocation and grain boundary analysis in the olivine structure.
Fichier principal
Vignette du fichier
main.pdf (36.38 Mo) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)
Licence

Dates et versions

hal-04875760 , version 1 (09-01-2025)

Licence

Identifiants

Citer

Jean Furstoss, Carlos Salazar, Philippe Carrez, Pierre Hirel, Julien Lam. All-around local structure classification with supervised learning: The example of crystal phases and dislocations in complex oxides. 2025. ⟨hal-04875760⟩
2 Consultations
0 Téléchargements

Altmetric

Partager

More