<?xml version='1.0' encoding='utf-8'?> <oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"> <dc:creator>Liyanage, Manura</dc:creator> <dc:creator>Reith, David</dc:creator> <dc:creator>Eyert, Volker</dc:creator> <dc:creator>Curtin, W. A.</dc:creator> <dc:date>2024-05-03</dc:date> <dc:description>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. Understanding the various effects of H requires studies with atomistic resolution but at scales that incorporate defects such as cracks, interfaces, and dislocations. Such studies thus demand accurate interatomic potentials. Here, a neural network potential (NNP) for the Zr-H system is developed within the Behler-Parrinello framework. The Zr-H NNP 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 behavior 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. This Zr-H NNP should thus be very powerful for future study of many phenomena driving H degradation in Zr that require atomistic detail at scales far above those accessible by first-principles</dc:description> <dc:identifier>https://archive.materialscloud.org/record/2024.68</dc:identifier> <dc:identifier>doi:10.24435/materialscloud:qv-xn</dc:identifier> <dc:identifier>mcid:2024.68</dc:identifier> <dc:identifier>oai:materialscloud.org:2171</dc:identifier> <dc:language>en</dc:language> <dc:publisher>Materials Cloud</dc:publisher> <dc:rights>info:eu-repo/semantics/openAccess</dc:rights> <dc:rights>Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights> <dc:subject>Zirconium Hydrides</dc:subject> <dc:subject>Neural network potentials</dc:subject> <dc:subject>molecular dynamics simulation</dc:subject> <dc:title>Neural network potential for Zr-H</dc:title> <dc:type>Dataset</dc:type> </oai_dc:dc>