Computational modeling plays a critical role in advancing metal-air battery systems by providing insights into complex electrochemical processes that are difficult to observe experimentally. Three primary approaches—density functional theory (DFT), molecular dynamics (MD), and continuum modeling—are employed to investigate oxygen reduction mechanisms, electrolyte stability, and precipitate formation. These methods enable researchers to optimize materials and predict long-term performance.

Density functional theory is widely used to study catalytic reactions in metal-air batteries, particularly the oxygen reduction reaction (ORR) and oxygen evolution reaction (OER). These reactions occur at the air cathode and dictate battery efficiency. DFT calculations assess the electronic structure of catalysts, adsorption energies of intermediates, and activation barriers for reaction steps. For example, studies on manganese oxide catalysts reveal that surface oxygen vacancies enhance ORR activity by lowering the energy barrier for O₂ dissociation. Similarly, cobalt-based catalysts exhibit improved OER performance due to optimal d-band center positioning, facilitating charge transfer. DFT also evaluates dopant effects, showing that nitrogen-doped graphene can enhance ORR kinetics by modifying carbon’s electronic environment. These insights guide the design of high-performance electrocatalysts.

Molecular dynamics simulations investigate electrolyte behavior, particularly decomposition pathways and interfacial stability. Aqueous electrolytes, such as potassium hydroxide (KOH), are common in zinc-air batteries, but their degradation limits cycle life. MD simulations track ion mobility, solvent structure, and reaction kinetics at electrode-electrolyte interfaces. For instance, simulations of zinc anodes in alkaline media show that hydroxide ions preferentially adsorb on zinc surfaces, leading to passivation layer formation. Non-aqueous electrolytes, like lithium-air battery systems with organic solvents, face challenges from solvent oxidation. MD studies reveal that dimethyl sulfoxide (DMSO) decomposes via nucleophilic attack by superoxide radicals, forming lithium carbonate deposits. These findings inform electrolyte additives and solvent selection to mitigate decomposition.

Continuum models address macroscopic phenomena, such as mass transport, precipitate growth, and pore clogging in metal-air batteries. These models solve coupled partial differential equations for species concentrations, electric potentials, and reaction rates across battery domains. In lithium-air systems, continuum frameworks predict lithium peroxide (Li₂O₂) deposition morphology, which affects capacity and reversibility. Simulations show that toroidal Li₂O₂ particles form under low discharge rates, while films dominate at high rates, aligning with experimental observations. For zinc-air batteries, models simulate zincate ion diffusion and ZnO precipitation in electrolytes, identifying concentration gradients as a key factor in dendrite formation. Continuum approaches also evaluate the impact of oxygen solubility and diffusivity on cathode performance, aiding flow field design in air electrodes.

Multiscale modeling integrates these methods to bridge atomic-level mechanisms with system-level behavior. For example, DFT-derived reaction kinetics can be incorporated into continuum models to improve ORR/OER rate predictions. Similarly, MD results on electrolyte decomposition feed into continuum frameworks to simulate side product accumulation. This holistic approach accelerates material discovery and system optimization.

Despite progress, challenges remain. DFT approximations, such as exchange-correlation functionals, may inaccurately predict reaction energetics for certain catalysts. MD simulations face limitations in timescales, often requiring reactive force fields to capture bond-breaking events. Continuum models rely on empirical parameters that may not generalize across battery designs. Future advancements in computational power and algorithm efficiency will enhance accuracy and enable larger-scale simulations.

In summary, computational modeling of metal-air batteries leverages DFT, MD, and continuum techniques to unravel reaction mechanisms, electrolyte degradation, and precipitate formation. These approaches provide a foundation for improving catalyst design, electrolyte stability, and system longevity, ultimately supporting the development of efficient and durable metal-air battery technologies.