HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Journal articles

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

Abstract : 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 (ANN) method or the boosted regression tree (BRT) 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.
Complete list of metadata

https://hal.univ-lorraine.fr/hal-03615718
Contributor : Stéphane Berbenni Connect in order to contact the contributor
Submitted on : Monday, March 21, 2022 - 5:38:53 PM
Last modification on : Friday, April 8, 2022 - 3:38:03 PM

File

 Restricted access
To satisfy the distribution rights of the publisher, the document is embargoed until : 2022-09-20

Please log in to resquest access to the document

Identifiers

Citation

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, Elsevier, 2022, 163, pp.104852. ⟨10.1016/j.jmps.2022.104852⟩. ⟨hal-03615718⟩

Share

Metrics

Record views

7