Magnesium (Mg) and its alloys, known for their low density and high specific strength, are increasingly explored as lightweight structural materials across a broad range of industrial applications. However, their widespread application remains constrained by intrinsic mechanical limitations, fundamentally rooted in the nature of crystallographic defects. Atomic-scale modeling techniques are transforming our ability to unravel the structures, energetics, and dynamics of these defects and to explore their complex interactions, thereby guiding defect engineering in Mg alloys. However, the growing body of available data can make it difficult for researchers to identify critical knowledge gaps and promising areas for further exploration. To address this challenge, we highlight key research domains with significant potential for impactful advancements, aiming to illuminate these areas while inspiring innovative approaches and encouraging deeper exploration of pivotal topics that may shape the future of Mg alloy development. This review presents a comprehensive overview of the state-of-the-art in atomic-scale modeling of defects in Mg and its alloys. We introduce key simulation methodologies, including density functional theory and atomistic simulations, and highlight their applications to defect distribution, defect dynamics, and defect-defect interactions. By bridging fundamental insights in defects with alloy design strategies, this review aims to support and inspire the broader Mg research community and to underscore the growing impact of atomic-scale modeling in the accelerated development of high-performance Mg alloys.

Grain boundary (GB) segregation substantially influences the mechanical properties and performance of magnesium (Mg). Atomic-scale modeling, typically using ab-initio or semi-empirical approaches, has mainly focused on GB segregation at highly symmetric GBs in Mg alloys, often failing to capture the diversity of local atomic environments and segregation energies, resulting in inaccurate structure-property predictions. This study employs atomistic simulations and machine learning models to systematically investigate the segregation behavior of common solute elements in polycrystalline Mg at both 0 K and finite temperatures. The machine learning models accurately predict segregation thermodynamics by incorporating energetic and structural descriptors. We found that segregation energy and vibrational free energy follow skew-normal distributions, with hydrostatic stress, an indicator of excess free volume, emerging as an important factor influencing segregation tendency. The local atomic environment’s flexibility, quantified by flexibility volume, is also crucial in predicting GB segregation. Comparing the grain boundary solute concentrations calculated via the Langmuir-McLean isotherm with experimental data, we identified a pronounced segregation tendency for Nd, highlighting its potential for GB engineering in Mg alloys. This work demonstrates the powerful synergy of atomistic simulations and machine learning, paving the way for designing advanced lightweight Mg alloys with tailored properties.