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Katherine Hollingsworth

Understanding and Predicting Hydrogen Embrittlement in Metals Demonstrates the Predictive Power of MLPs for Complex Metallurgical Phenomena




Post-cleavage configuration for fracture of ε-ZrH2 (crack plane (011), crack line <100>) computed using the Zr-H NNP. Atomic arrangement of Zr (cyan) and H (red) atoms (left) and orientation configuration with insets showing the orientation variants of ε-ZrH2 in the respective regions (right).



A new publication on the generation and application of an MLP for ZrH, co-authored by David Reith and Volker Eyert from Materials Design, as well as Manura Liyanage and W. A. Curtin from EPFL, has just been published in the Journal of Nuclear Materials.

 

 

Abstract:

The introduction of Hydrogen (H) into Zirconium (Zr) influences many mechanical properties, especially due to low H solubility and easy formation of Zirconium hydride phases. In this work, a neural network potential (NNP) for the Zr-H system has been developed, which retains the accuracy of a recent NNP for hcp Zr and exhibits excellent agreement with first-principles density functional theory (DFT) for (i) H interstitials and their diffusion in hcp Zr, (ii) formation energies, elastic constants, and surface energies of relevant Zr hydrides, and (iii) energetics of a common Zr/Zr-H interface. The Zr-H NNP shows physical behaviour for many different crack orientations in the most-stable ε-hydride, and structures and reasonable relative energetics for the ⟨a⟩ screw dislocation in pure Zr. The generated Zr-H NNP provides a very powerful for future studies of many phenomena driving H degradation in Zr that require atomistic detail at scales far above those accessible by first-principles.

 

M. Liyanage, D. Reith, V. Eyert, and W. A. Curtin, Neural network potential for Zr-H, J. Nucl. Mater. 602, 155341 (2024). https://doi.org/10.1016/j.jnucmat.2024.155341

 






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