Publication date: Jan 30, 2023
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.
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|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.|
|2023.21 (version v1) [This version]||Jan 30, 2023||DOI10.24435/materialscloud:vy-02|