Evaluating the applicability of classical and neural network interatomic potentials for modeling body centered cubic polymorph of magnesium. (1st June 2022)

• Record Type: Journal Article
• Authors: F Troncoso, Javier; Turlo, Vladyslav
• Is Part Of: Modelling and simulation in materials science and engineering. Volume 30:Number 4(2022)
• Publication Date: 2022-06-01
• Subjects: magnesium -- metastable polymorph -- interatomic potentials; Materials -- Mathematical models -- Periodicals; Matériaux -- Modèles mathématiques -- Périodiques; Materials -- Mathematical models; Periodicals; 620.00113
• DOI: 10.1088/1361-651X/ac5ebc
• Languages: English
• ISSNs: 0965-0393

Abstract

Abstract: Magnesium (Mg) is one of the most abundant metallic elements in nature and presents attractive mechanical properties in the industry. Particularly, it has a low density and relatively high strength/weight and stiffness/weight ratios, which make it one of the most attractive lightweight metals. However, the huge potential of Mg is restricted by its low ductility, associated with its hexagonal close packed (hcp) structure. This problem can be solved if Mg adopts the body centered cubic (bcc) structure, which is stable at high pressure or in confinement with stiff bcc metals like Nb. Molecular dynamics method is a magnificent tool to study material's structure and deformation mechanisms at the atomic level, however, requiring accurate interatomic potentials. The majority of the interatomic potentials available in the literature for Mg have only been fitted to the properties of its stable hcp phase. In the present work, we perform systematic study of applicability of currently available Mg potentials to modeling the properties of metastable bcc polymorph of Mg, taking into account cohesive energy curves, elastic constants, stacking fault energies, and phonon dispersion curves. We conclude that the modified embedded atom method (MEAM) potentials are the most suitable for investigating bcc Mg in Mg/Nb nano-composites, while the properties of high-pressure bcc Mg would be better modeled by neural network interatomic potentials after different local atomic environments corresponding to bcc Mg being included into the fitting database.