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

Abstract

Designing cathode materials is crucial for developing advanced Li–S batteries, but conventional trial-and-error methods are time- and resource-intensive. This study employs machine learning (ML) with feature analysis, data augmentation, and backward prediction using particle swarm optimization (PSO) for rapid discovery and inverse design of cathode materials. The predictive model with XGBoost achieved high accuracy with a determination coefficient (R² = 0.8345) and a mean absolute error (MAE = 4.48%) in estimating capacity retention. The PSO-based backward prediction identified titanium (Ti) and 2-methylimidazole (2-MeIM) as optimal MOF precursors. A Ti/2-MeIM MOF is synthesized in the form of Ti/Zn-ZIF via 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.