Multiscale simulation approaches have become indispensable tools for understanding and optimizing aqueous battery systems. These methods bridge atomic-level phenomena with macroscopic performance, addressing critical challenges unique to water-based electrolytes, including hydrogen evolution, pH-dependent reactions, and dynamic solvation structures. Zinc-ion and sodium-ion aqueous batteries serve as prime examples where such simulations provide insights inaccessible through experimentation alone.

At the atomic scale, density functional theory (DFT) calculations reveal the thermodynamics of hydrogen adsorption on electrode surfaces, a key factor in parasitic hydrogen evolution. For zinc anodes, simulations show that surface orientation significantly affects hydrogen overpotential, with the (002) plane exhibiting higher resistance to gas evolution compared to (100) or (101) planes. Solvation free energy calculations quantify the stability of Zn²⁺ and Na⁺ ions in aqueous environments, showing how water molecules arrange in primary and secondary solvation shells. Molecular dynamics (DFT-MD) simulations capture the dynamic nature of these solvation structures under electric fields, demonstrating how applied potential distorts coordination geometries during charge transfer.

Moving to mesoscale simulations, kinetic Monte Carlo methods model dendrite growth in zinc batteries by incorporating diffusion-limited aggregation processes. These simulations account for pH gradients near the electrode-electrolyte interface, showing how localized acidity accelerates tip-driven dendrite propagation. Phase-field models couple electrochemical reactions with morphological evolution, reproducing experimentally observed mossy and needle-like zinc deposits under different current densities. For sodium-ion systems, continuum models solve Nernst-Planck-Poisson equations to predict concentration polarization effects, revealing how sodium ion depletion near the cathode limits high-rate performance.

Macroscale cell-level models integrate these phenomena through coupled electrochemical-thermal formulations. Newman-type porous electrode theory is adapted for aqueous systems by incorporating pH-dependent side reactions. The models solve for species conservation of H⁺ and OH⁻ ions alongside Na⁺ or Zn²⁺ transport, capturing how electrolyte acidity evolves during cycling. Thermal coupling becomes crucial as hydrogen evolution generates heat, creating feedback loops that accelerate degradation. Finite volume methods discretize these equations across battery domains, predicting temperature and current distribution in practical cell geometries.

Validation of multiscale models relies on advanced in situ characterization. X-ray diffraction computed tomography provides spatial resolution of phase transformations in zinc anodes during cycling, confirming simulated deposition patterns. Raman microspectroscopy maps pH gradients predicted by continuum models, showing agreement within 0.5 pH units near electrode surfaces. Quasi-elastic neutron scattering validates simulated water dynamics in solvation shells, with measured diffusion coefficients matching predictions within 10% error for sodium-ion electrolytes. These techniques create feedback loops where experimental data refine force fields in atomistic simulations and boundary conditions in continuum models.

For zinc-ion batteries, multiscale simulations have identified electrolyte additives that suppress hydrogen evolution through competitive adsorption. DFT screening of organic molecules reveals those with adsorption energies between -0.8 eV to -1.2 eV on zinc surfaces effectively block water reduction sites without hindering Zn²⁺ deposition. Mesoscale modeling shows how these additives homogenize ion flux, reducing dendrite formation rates by over 60% at 5 mA/cm² current density. At the cell level, simulations optimize separator porosity to balance zincate crossover and ionic conductivity, achieving coulombic efficiencies above 98% in validated designs.

In sodium-ion aqueous systems, simulations address cathode stability challenges. Ab initio molecular dynamics tracks proton co-intercalation in Prussian blue analogs, showing how lattice distortions above 2% strain accelerate transition metal dissolution. Coarse-grained models predict the percolation threshold for conductive additives in polyanionic cathodes, guiding compositions that maintain electronic connectivity during cycling. Cell-level simulations couple these cathode models with sodium metal oxide anodes, identifying electrolyte pH ranges (8-10) that simultaneously stabilize both electrodes while minimizing gas evolution.

Recent advances combine machine learning with traditional multiscale methods. Neural network potentials trained on DFT datasets enable nanosecond-scale simulations of electrolyte decomposition at interfaces. Graph neural networks predict solid-electrolyte interphase composition from molecular descriptors, accelerating the screening of protective coatings. These hybrid approaches reduce computational costs while maintaining accuracy, enabling high-throughput exploration of aqueous battery chemistries.

Challenges remain in fully capturing transient phenomena. The timescale gap between picosecond-scale solvation dynamics and hour-long cycling processes requires innovative coarse-graining techniques. Progress in reactive force fields and accelerated sampling methods continues to bridge this gap. Another frontier involves coupling mechanical stress models with electrochemical simulations, as aqueous systems often exhibit significant volume changes during phase transitions.

The integration of multiscale simulations with experimental workflows is advancing aqueous battery development. Virtual prototyping reduces trial-and-error in electrolyte formulation, while predictive degradation models guide lifetime optimization. As computational power grows and methods refine, these approaches will play an increasingly central role in unlocking the potential of sustainable aqueous energy storage systems.