Poor plasticity is an intrinsic disadvantage of magnesium (Mg) alloys, which limits their wide application at room temperature. Alloying is an accepted method to tune the plastic deformation mode and improve plasticity. However, the effect of solute atoms on the activation of different dislocations is still unclear and has rarely been systematically investigated in Mg alloys. In this work, the formulations of Peierls-Nabarro stresses (σ_p) for edge and screw dislocations along various slip planes in Mg-X (X = Y, Ca, Nd, Zn, Al and Sn) alloys are firstly derivate, as well as the calculation of the parameter K (energy factor) based on the first-principles calculation. The effects of solute atoms on the σ_p of various types of dislocations are systematically studied. The difference of the σ_p between the Mg-X alloy and pure Mg, i.e., Δσ_p, is determined, which is strongly influenced by the solute atoms. The negative Δσ_p reflects the promotion of dislocation activation. The relationship between the Δσ_p of different non-basal dislocations and elongation in eight Mg-X alloys is explored. The simultaneous improvement of the activation of the prismatic 〈a〉 and the pyramidal 〈c + a〉 dislocations is discovered, which can be achieved by specific alloying elements. Cooperative activation of the prismatic 〈a〉 and the pyramidal 〈c + a〉 dislocations owing to the reduced Δσ_p is shown to closely correlate with the significant increased plasticity of the Mg alloys. These findings advance a novel perspective on alloy design strategies for Mg alloys with improved plasticity.

Accurate predictions of the dislocation behavior of magnesium (Mg) by molecular dynamics (MD) simulations are essential for studying the fundamental mechanisms of deformation and designing high plasticity Mg alloys. However, existing atomic potentials in MD simulation for Mg are not sufficiently quantitative for many dislocations-associated phenomena, such as stacking fault energy (SFE) and dislocation core structures. Here, by combining 468 density functional theory (DFT) calculated data points and a machine learning method, we create a broadly applicable deep learning potential (DLP) to study the dislocation behavior of Mg. We demonstrate that our DLP reproduces the SFE, lattice constants, elastic constants, and surface energies in reasonable agreement with experimental or DFT data. Furthermore, the DLP predicted basal 〈a〉, prismatic 〈a〉, pyramidal 〈c + a〉 dislocations all agree well with DFT results on dissociation distance and core structures. Importantly, the DLP has a superior performance on distinguishing the pyramidal Ⅰ and Ⅱ 〈c + a〉 screw dislocation core structures. Our results show that the DLP is suitable for investigating the dislocation behavior of Mg, making it valuable for future realistic atomistic studies of general deformation problems.