Lithium-sulfur (Li-S) batteries are a promising next-generation energy storage technology due to their high theoretical energy density and low material cost. However, their commercialization is hindered by challenges such as polysulfide shuttling, electrolyte degradation, and rapid capacity fade. Computational modeling plays a critical role in understanding these mechanisms, enabling the design of improved Li-S systems without relying solely on experimental trial and error. Key computational approaches include density functional theory (DFT), molecular dynamics (MD), and continuum models, each providing unique insights into Li-S battery behavior.

Density functional theory is a quantum mechanical method used to investigate the electronic structure of materials at the atomic scale. In Li-S batteries, DFT helps elucidate the thermodynamic stability of lithium polysulfides (LiPS), their adsorption on host materials, and the redox reactions occurring during charge and discharge. For instance, DFT calculations reveal that long-chain polysulfides (Li₂S₆, Li₂S₈) are more stable than short-chain species (Li₂S₂, Li₂S₄) in the electrolyte, contributing to the shuttling effect. By simulating the binding energies between polysulfides and potential anchoring materials such as metal oxides, doped carbons, or conductive polymers, DFT identifies candidate materials that can mitigate shuttling. Sulfur’s insulating nature is another challenge, and DFT predicts how different conductive matrices interact with sulfur to improve electronic conductivity. Additionally, DFT examines the decomposition pathways of electrolytes, identifying solvents and additives that enhance stability against reactive polysulfides.

Molecular dynamics simulations extend these insights by modeling the dynamic behavior of polysulfides and electrolyte components over time. MD tracks the diffusion of LiPS in the electrolyte, providing quantitative data on their mobility and interactions with solvents like DOL/DME (1,3-dioxolane and 1,2-dimethoxyethane). Simulations show that polysulfide diffusion coefficients range from 10⁻⁷ to 10⁻⁶ cm²/s, depending on solvent composition and salt concentration. MD also captures the solvation structure of LiPS, revealing how lithium ions coordinate with ether-based solvents and anions like LiTFSI. This information is crucial for optimizing electrolyte formulations to reduce polysulfide solubility and shuttling. Furthermore, MD investigates the role of interfaces, such as the solid-electrolyte interphase (SEI) on the lithium anode. By modeling SEI formation and evolution, MD predicts how polysulfides corrode the anode and how protective layers can mitigate degradation.

Continuum models bridge atomic-scale phenomena with macroscopic battery performance. These models solve coupled partial differential equations for mass transport, charge transfer, and electrochemical reactions across the cell. A typical continuum framework includes:
- Sulfur reduction reactions: Describing the stepwise conversion of S₈ to Li₂S through intermediate polysulfides.
- Polysulfide diffusion: Accounting for concentration gradients in the electrolyte and porous electrodes.
- Precipitation/dissolution: Modeling the nucleation and growth of Li₂S, a critical process affecting capacity and kinetics.
- Electron and ion transport: Simulating conductivity limitations in the sulfur cathode and separator.

Continuum simulations quantify the impact of parameters like electrolyte/sulfur ratio, electrode porosity, and current density on discharge curves and capacity fade. For example, they show that high sulfur loading exacerbates polarization due to poor ion transport, while lean electrolytes accelerate polysulfide diffusion to the anode. Degradation mechanisms are also captured, such as the irreversible loss of active material due to Li₂S accumulation or electrolyte depletion. By coupling these effects, continuum models predict cycle life and guide cell design.

A critical challenge in Li-S batteries is the polysulfide shuttle, which leads to self-discharge and low coulombic efficiency. Multiscale modeling combines DFT, MD, and continuum approaches to address this issue. DFT identifies strong-binding materials, MD evaluates their effectiveness in trapping polysulfides, and continuum models assess the overall impact on battery performance. For instance, simulations demonstrate that porous carbon hosts with nitrogen doping enhance polysulfide retention, reducing shuttle effects. Similarly, modeling reveals how selective membranes or electrolyte additives can block polysulfide migration while permitting lithium-ion transport.

Degradation mechanisms are another focus of computational studies. DFT predicts the thermodynamic instability of electrolytes in the presence of polysulfides, while MD simulates their decomposition reactions at the anode. Continuum models integrate these findings to predict capacity fade over cycles. For example, simulations show that anode passivation by Li₂S increases overpotential, while electrolyte oxidation at the cathode depletes active lithium. By quantifying these processes, models inform strategies to extend cycle life, such as optimizing the electrolyte composition or designing protective interlayers.

In summary, computational modeling provides a powerful toolkit for understanding and optimizing Li-S batteries. DFT reveals atomic-scale interactions, MD tracks dynamic processes, and continuum models predict system-level behavior. Together, they accelerate the development of Li-S technologies by identifying material solutions, mitigating degradation, and guiding cell design. Future advancements will involve tighter integration of these methods, enabling predictive simulations that bridge scales from atoms to full cells. Such efforts will be essential for realizing the high energy density and long cycle life required for commercial Li-S batteries.