Theoretical modeling and intelligent design of electrocatalysts for liquid lithium-sulfur batteries

Lithium-sulfur batteries are considered a highly promising next-generation secondary battery, but are limited by polysulfide shuttle effect. Electrocatalysis can mitigate polysulfide shuttle effect. Density functional theory (DFT) and machine learning (ML) have been applied to explore electrocatalysts for lithium-sulfur battery cathodes, making significant contributions to the virtual screening of promising cathode electrocatalysts and understanding of sulfur reduction reaction mechanisms. However, these latest developments have not been summarized in a timely manner. This article reviews the applications of DFT calculations and ML in exploring electrocatalysts for lithium-sulfur battery cathodes. DFT calculations are summarized from the perspectives of structural stability, electronic structure, ionic/molecule adsorption and diffusion simulation, and electrocatalytic activity to explore electrocatalysts for lithium-sulfur battery cathodes. The application of ML in exploring electrocatalysts for lithium-sulfur batteries is reviewed from the perspectives of enabling high-throughput screening of electrocatalyst candidates, guiding the synthesis optimization of promising candidates, and decoding the general structure-activity relationships from the accumulated data. The evaluation and validation of popular functionals is also discussed, and the selection of density functional methods, the uncertainty of computational tools, existing problems, and future development directions are analyzed. This work is expected to provide theoretical insights for the rational design and mechanism analysis of electrocatalysts for lithium-sulfur battery cathodes.