Manura Liyanage^1*, David Reith², Volker Eyert², W. A. Curtin¹

1 Laboratory for Multiscale Mechanics Modelling, Ecole Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland

2 Materials Design SARL, 42 avenue Verdier, 92120 Montrouge, France

* Corresponding authors emails: pandula.liyanage@epfl.ch

DOI10.24435/materialscloud:vy-02 [version v1]

Publication date: Jan 30, 2023

How to cite this record

Manura Liyanage, David Reith, Volker Eyert, W. A. Curtin, Machine learning for metallurgy V: A neural-network potential for zirconium data of published plots, Materials Cloud Archive 2023.21 (2023), doi: 10.24435/materialscloud:vy-02.

Description

The mechanical performance—including deformation, fracture, and radiation damage—of zirconium is determined at the atomic scale. With Zr and its alloys extensively used in the nuclear industry, understanding that atomic-scale behavior is crucial. The defects controlling that performance are at size scales far larger than accessible by first-principles methods, necessitating the use of semi-empirical interatomic potentials. Existing potentials for Zr are not sufficiently quantitative, nor easily extendable to alloys, oxides, or hydrides. To overcome these issues, a neural network machine learning potential (NNP) is developed here within the Behler-Parrinello framework for Zr. With a careful choice of descriptors of the atomic environments and the creation of a first-principles training dataset that includes a wide spectrum of configurations of metallurgical relevance, a very accurate NNP is demonstrated. Specifically, the Zr NNP yields a good description of dislocation structures and their relative energies and fracture behavior, along with bulk, surface, and point-defect properties and structures, and significantly outperforms the best available traditional potentials. Results here will enable large-scale simulations of complex processes and provide the basis for future extensions to alloys, oxides, and hydrides.

Materials Cloud sections using this data

Files

File name	Size	Description
Data_Liyanage_PRM_2022.zip MD5md5:0234c86630388b84c96950474536d9f1	105.3 KiB	The archive contains the data points for the plots published in the paper. Th files contain the .tex (LaTeX) files used to develop the plots. A description of each figure is given in README.txt.

License

Files and data are licensed under the terms of the following license: Materials Cloud non-exclusive license to distribute v1.0.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

External references

Keywords

machine learning Zirconium Defects Ductility Fracture MARVEL