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Lithium‐sulfur batteries (LSBs) have attracted increasing attention in the past decades due to their great potential to the next‐generation high‐energy‐density storage systems. As important as electrodes, electrolytes that could strongly determine battery performance via component regulation have left big difficulties in clarifying their complex interactions caused by multicomponent as well as the intricate formation mechanism of passivation layers at the electrolyte‐electrode interfaces. Fortunately, machine learning (ML), which is time‐saving and highly efficient, has played an irreplaceable role in accelerating discovery, design, and optimization of novel electrochemical energy storage materials. In this concept, we summarize the complex issues present in multicomponent electrolytes and focus on optimization of electrolyte formulations based on ML in order to provide an outlook on the recent development and perspectives on LSBs from the viewpoint of high‐performance electrolytes. [ABSTRACT FROM AUTHOR]

Presents results of comprehensive full-dimensional (6D) quantum calculations of the rovibrational levels of hydrogen halide dimers for total angular momentum. Use of 6D potential energy surfaces in the calculations; Importance of including the intramolecular vibrations of two hydrochloric acid subunits in the comparison with four-dimensional bound state calculations.

Presents information on a study which examined the polarization effect in the measurement of thin wire diameter by laser diffraction. Methodology of the study; Results and discussion; Conclusion.

Lithium‐sulfur batteries (LSBs) have become one of the most powerful candidates for next‐generation battery technologies due to their high theoretical energy density and low cost. However, the notorious shuttle effect of soluble lithium polysulfides (LiPSs) and sluggish conversion reaction kinetics cause low sulfur utilization and inferior cycle life. Rational catalyst design on hierarchical pore structures and composition optimization is highly desired to realize synergetic enrichment, accommodation, and catalytic redox capacity of sulfur species. In this consideration, the hollow and porous N‐doped carbon framework is prepared, in which Co nanoparticles (NPs) are evenly embedded (denoted as Co‐HMCF) to modulate electron cloud density of carbons. Electrochemical tests and density functional theory (DFT) calculations demonstrate that Co‐HMCF could simultaneously deliver superior catalytic activity in accelerating LiPSs conversion as well as Li2S nucleation/decomposition to improve overall sulfur redox kinetics. Consequently, the Co‐HMCF interlayer significantly improves the battery performance, including high discharge capacity output (1538 mAh g−1 at 0.2 C), stable long‐term cycle (0.047% capacity decay per cycle for 800 cycles at 1.0 C), and exceptional rate capacity (582 mAh g−1 at 5.0 C). [ABSTRACT FROM AUTHOR]