Computational methods have become indispensable tools in the development of magnesium battery technologies, addressing key challenges such as Mg2+ ion mobility, electrode-electrolyte compatibility, and material stability. Density functional theory (DFT), molecular dynamics (MD), and multiscale modeling techniques are particularly valuable for probing the unique behavior of magnesium systems, where the divalent nature of Mg2+ introduces distinct thermodynamic and kinetic considerations compared to monovalent ions like Li+.

A primary focus of computational studies is predicting Mg2+ diffusion barriers in potential electrode materials. The high charge density of Mg2+ results in strong Coulombic interactions with host lattices, often leading to sluggish diffusion kinetics. DFT calculations have systematically evaluated migration pathways in candidate materials such as Chevrel-phase Mo6S8, spinel oxides, and layered sulfides. For example, DFT studies revealed that Mg2+ diffusion in Mo6S8 occurs through interconnected low-energy pathways with barriers around 500-600 meV, consistent with experimental measurements. These insights guided the optimization of Chevrel-phase compositions to enhance ionic conductivity. Similarly, DFT screening of transition metal oxides identified Cr2O4 and Mn2O4 as promising spinel frameworks with calculated Mg2+ migration barriers below 800 meV, prompting experimental groups to synthesize and test these materials.

Interface stability between magnesium anodes and electrolytes presents another critical challenge addressed through computational modeling. Ab initio MD simulations have elucidated decomposition mechanisms at Mg/electrolyte interfaces, showing how chloride-containing electrolytes form stable solid-electrolyte interphases (SEI) by promoting the formation of MgCl2-rich passivation layers. These simulations explained experimental observations of improved cycling efficiency with Cl− additives. DFT studies have also predicted the thermodynamic stability windows of various electrolyte solvents against Mg metal, revealing that ether-based solvents exhibit higher reduction stability compared to carbonate analogues—a finding corroborated by electrochemical testing.

Material screening approaches leveraging high-throughput DFT calculations have accelerated the discovery of magnesium battery components. Researchers have employed descriptor-based screening of thousands of compounds using metrics such as Mg insertion voltage, thermodynamic stability, and volume change upon magnesiation. One study computationally evaluated over 15,000 inorganic materials, identifying 20 previously unexplored candidates with predicted voltages above 2 V versus Mg/Mg2+ and low diffusion barriers. Experimental validation confirmed several predictions, including high capacity in MgMnSiO4 and MgVPO4F. Machine learning models trained on DFT datasets have further enhanced screening efficiency by predicting properties of hypothetical materials without full quantum mechanical calculations.

Multiscale modeling bridges atomic-scale insights with macroscopic performance predictions in magnesium batteries. Continuum models parameterized with DFT-calculated transport properties have simulated cell-level behavior under various operating conditions. For instance, phase-field models incorporating Mg2+ diffusion anisotropy in layered sulfides have reproduced experimentally observed polarization patterns and informed electrode architecture design. Coupled electrochemical-thermal models have predicted heat generation rates in prototype cells, guiding thermal management strategies for high-power applications.

Case studies demonstrate how computational guidance has advanced specific magnesium battery components. In cathode development, DFT calculations predicted that substituting S with Se in TiS2 would lower Mg2+ diffusion barriers by expanding interlayer spacing. Experimental synthesis of TiSe2 confirmed a threefold improvement in rate capability compared to the sulfide analogue. For electrolytes, MD simulations revealed that Mg(TFSI)2 in glyme solvents forms stable solvation structures with low dissociation energies, explaining superior ionic conductivity measurements. This understanding prompted optimization of salt concentration and solvent chain length to maximize conductivity while maintaining electrochemical stability.

In anode research, DFT studies exposed the thermodynamic instability of magnesium with common current collectors, predicting alloying reactions with aluminum but compatibility with stainless steel. Subsequent experimental work validated these predictions, leading to revised cell assembly protocols. Interface engineering has also benefited from computational insights—ab initio calculations showed that atomic layer deposition of Al2O3 on Mg surfaces could suppress dendrite formation by promoting uniform nucleation, a strategy successfully implemented in symmetric cell tests showing extended cycle life.

Challenges remain in accurately modeling certain aspects of magnesium battery behavior. The strong polarization effects of Mg2+ require advanced DFT functionals beyond standard generalized gradient approximation (GGA) to properly describe electron correlation. Solvation models continue to evolve to capture the complex coordination chemistry of Mg2+ in electrolytes. Multiscale models need improved parameterization of side reaction kinetics at interfaces. Despite these limitations, computational methods have proven their value in the magnesium battery field by providing fundamental understanding, predicting promising materials, and reducing trial-and-error experimentation. As both modeling techniques and experimental characterization advance, their integration will remain essential for realizing practical high-performance magnesium energy storage systems.