Invariant surface elastic properties in FCC metals and their correlation to bulk properties revealed by machine learning methods - Université de Lorraine Accéder directement au contenu
Article Dans Une Revue Journal of the Mechanics and Physics of Solids Année : 2022

Invariant surface elastic properties in FCC metals and their correlation to bulk properties revealed by machine learning methods

Résumé

We present a combination of machine-learned models that predicts the surface elastic properties of general free surfaces in face-centered cubic (FCC) metals. These models are built by combining a semi-analytical method based on atomistic simulations to calculate surface properties with the artificial neural network or the boosted regression tree method. The latter is also used to link bulk properties and surface orientation to surface properties. The surface elastic properties are represented by their invariants considering plane elasticity within a polar method. The resulting models are shown to accurately predict the surface elastic properties of seven pure FCC metals (Cu, Ni, Ag, Au, Al, Pd, Pt). The BRT model reveals the correlations between bulk and corresponding surface properties in terms of invariants, which can be used to guide the design of complex nano-sized particles, wires and films. Finally, by expressing the surface excess energy density as a function of surface elastic invariants, fast predictions of surface energy as a function of in-plane deformations can be made from these model constructs.
Fichier principal
Vignette du fichier
JMPS_SurfaceElasticity.pdf (14.47 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03527509 , version 2 (15-01-2022)
hal-03527509 , version 1 (21-03-2022)
hal-03527509 , version 3 (11-11-2022)

Licence

Paternité - Pas d'utilisation commerciale - Pas de modification

Identifiants

Citer

Xiaolei Chen, Rémi Dingreville, Thiebaud Richeton, Stéphane Berbenni. Invariant surface elastic properties in FCC metals and their correlation to bulk properties revealed by machine learning methods. Journal of the Mechanics and Physics of Solids, 2022, 163, pp.104852. ⟨10.1016/j.jmps.2022.104852⟩. ⟨hal-03527509v3⟩
104 Consultations
291 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More