Machine learning-assisted design of cathode materials for lithium–sulfur batteries derived from a metal–organic framework

post synthetic exchange, and subsequent carbonization yields Ti-derivative embedded nitrogen-doped carbon (NC-Ti) as a sulfur host material (S@NC-Ti). S@NC-Ti demonstrated average capacity retentions of 62.3%, 72.1%, and 65.3% at 0.1 C, 0.5 C, and 1.0 C, respectively, aligning with ML predictions. Furthermore, forward prediction successfully anticipated a capacity retention of 75.16% for the Ti/Zn bimetallic ZIF, a carbon precursor, at 1.5 C with a 65 wt% sulfur-carbon ratio, matching the experimental result of 84.13% within a 12% error margin. This study highlights the potential of ML-driven approaches in accelerating cathode material development for Li-S batteries.