<?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>Liu, Xin</dc:creator> <dc:creator>Rahbar Niazi, Masoud</dc:creator> <dc:creator>Liu, Tao</dc:creator> <dc:creator>Yin, Binglun</dc:creator> <dc:creator>Curtin, William</dc:creator> <dc:date>2023-01-17</dc:date> <dc:description>Prismatic slip in magnesium at temperatures T ≲ 150 K occurs at ∼ 100 MPa independent of temperature, and jerky flow due to large prismatic dislocation glide distances is observed; this athermal regime is not understood. In contrast, the behavior at T ≳ 150 K is understood to be governed by a thermally-activated double-cross-slip of the stable basal screw dislocation through an unstable or weakly metastable prism screw configuration and back to the basal screw. Here, a range of neural network potentials (NNPs) that are very similar for many properties of Mg including the basal-prism-basal cross-slip path and pro- cess, are shown to have an instability in prism slip at a potential-dependent critical stress. One NNP, NNP-77, has a critical instability stress in good agreement with experiments and also has basal-prism-basal transition path energies in very good agreement with DFT results, making it an excellent potential for understanding Mg prism slip. Full 3d simulations of the expansion of a prismatic loop using NNP-77 then also show a transition from cross-slip onto the basal plane at low stresses to prismatic loop expansion with no cross- slip at higher stresses, consistent with in-situ TEM observations. These results reveal (i) the origin and prediction of the observed unstable low-T prismatic slip in Mg and (ii) the critical use of machine-learning potentials to guide discovery and understanding of new important metallurgical behavior.</dc:description> <dc:identifier>https://archive.materialscloud.org/record/2023.10</dc:identifier> <dc:identifier>doi:10.24435/materialscloud:3f-w3</dc:identifier> <dc:identifier>mcid:2023.10</dc:identifier> <dc:identifier>oai:materialscloud.org:1614</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>Magnesium</dc:subject> <dc:subject>Prismatic slip</dc:subject> <dc:subject>Neural Network potential</dc:subject> <dc:subject>Minimum energy path</dc:subject> <dc:subject>MARVEL</dc:subject> <dc:title>A low-temperature prismatic slip instability in Mg understood using machine learning potentials</dc:title> <dc:type>Dataset</dc:type> </oai_dc:dc>