Title :

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

Author(s) :

Furstoss, Jean [Auteur]
Unité Matériaux et Transformations (UMET) - UMR 8207
Institut Pprime [UPR 3346] [PPrime [Poitiers]]
Salazar, Carlos R. [Auteur]
Unité Matériaux et Transformations - UMR 8207 [UMET]
Carrez, Philippe [Auteur]
Unité Matériaux et Transformations (UMET) - UMR 8207
Hirel, Pierre [Auteur]
Unité Matériaux et Transformations (UMET) - UMR 8207
Lam, Julien [Auteur]
Unité Matériaux et Transformations (UMET) - UMR 8207

Journal title :

Computer Physics Communications

Abbreviated title :

Computer Physics Communications

Volume number :

309

Pages :

109480

Publisher :

Elsevier

Publication date :

2025-01-08

ISSN :

0010-4655

HAL domain(s) :

Physique [physics]/Matière Condensée [cond-mat]/Science des matériaux [cond-mat.mtrl-sci]
Planète et Univers [physics]/Sciences de la Terre

English abstract : [en]

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 ...
Show more >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.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

European Project :

Rheology of Earth materials: closing the gap between TIMEscales in the laboratory and in the MANtle

ANR Project :

ULNE
Science et Ingénierie à l'Echelle Nano
Nucléation dans les synthèses de nanoparticules: Simulations atomistiques assistées par machine-learning

Administrative institution(s) :

Université de Lille
CNRS
INRAE
ENSCL

Collections :

• Unité Matériaux et Transformations (UMET) - UMR 8207

Research team(s) :

Plasticité

Submission date :

2025-01-16T12:20:31Z
2025-01-16T15:19:02Z