Current browse context:
cond-mat.mtrl-sci
Change to browse by:
References & Citations
Condensed Matter > Materials Science
Title: A collinear-spin machine learned interatomic potential for Fe$_{7}$Cr$_{2}$Ni alloy
(Submitted on 15 Sep 2023 (v1), last revised 8 Mar 2024 (this version, v2))
Abstract: We have developed a new machine learned interatomic potential for the prototypical austenitic steel Fe$_{7}$Cr$_{2}$Ni, using the Gaussian approximation potential (GAP) framework. This new GAP can model the alloy's properties with close to density functional theory (DFT) accuracy, while at the same time allowing us to access larger length and time scales than expensive first-principles methods. We also extended the GAP input descriptors to approximate the effects of collinear spins (Spin GAP), and demonstrate how this extended model successfully predicts structural distortions due to antiferromagnetic and paramagnetic spin states. We demonstrate the application of the Spin GAP model for bulk properties and vacancies and validate against DFT. These results are a step towards modelling the atomistic origins of ageing in austenitic steels with higher accuracy.
Submission history
From: Lakshmi Shenoy [view email][v1] Fri, 15 Sep 2023 18:28:40 GMT (1212kb,D)
[v2] Fri, 8 Mar 2024 10:27:13 GMT (423kb,D)
Link back to: arXiv, form interface, contact.