Machine learning-driven high-throughput prediction of Hg⁰ capture in MOFs and decipherment of structure-property...

Metal-organic frameworks (MOFs) show potential for Hg⁰ adsorption via tunable pore structures and chemical functionalities. However, efficient screening of MOF adsorbents remains challenging due to vast material space and unclear structure-property relationships. Herein, a Hg⁰ adsorption database of 6528 MOF samples was established, and machine learning models were developed for predicting adsorption performance. Among tested models, extreme gradient boosting (XGBoost) exhibited the best prediction on the independent test set, achieving a coefficient of determination (R²) of 0.893. SHAP analysis revealed that energy descriptors, including logarithmic Henry coefficient (log(K_H)) and adsorption enthalpy (ΔH_ads), dominated Hg⁰ adsorption, while structural descriptors further regulated adsorption behavior. Combined model interpretation revealed a condition-dependent interaction between adsorption affinity and pore characteristics. High-throughput screening revealed characteristic descriptor intervals for high-performing MOFs, including log(K_H)=-3.62 to -2.61, ΔH_ads = -34.60 to -25.21 kJ·mol^-1, and VF = 0.67-0.78. MOFid-based structural analysis identified significant enrichment of Cr, Fe, Co nodes and nbo topology, alongside preferred acs and tbo topologies, as well as nitrogen-containing aromatic conjugated heterocyclic ligands in high-performing MOFs. These findings clarify the roles of metal nodes, topology, and ligand chemistry in Hg⁰ adsorption and provide a machine learning-driven strategy for rational design of MOF-based mercury removal adsorbents.