Integrating Molecular Simulations, Interpretable Machine Learning and Metal-Organic Framework Identifier Analysis for the Discovery of Hydrophobic Metal-Organic Frameworks with High Adsorption Capacity for Sulfur Mustard Simulants

Mustard gas (HD) is a highly toxic blistering chemical warfare agent that poses risks to health and safety.To enable safer investigation, experimental studies commonly use simulants such as diethyl sulfide (DES) as substitutes.However, molecular simulation studies on HD simulants remain limited.This work integrates molecular simulations, machine learning (ML), and metal-organic framework identifier (MOFid) analysis to identify hydrophobic metal-organic frameworks (MOFs) with DES adsorption capacity and explore structure-performance relationships.Univariate analysis evaluates