Interpretable Machine Learning Framework for Gas Adsorption Prediction and Screening on Transition Metal Dichalcogenides

Two-dimensional transition metal dichalcogenides (TMDs) are promising gas-sensing materials, but adsorption behavior across doped host-dopant-gas spaces remains difficult to predict and interpret. Here, we develop a descriptor-informed machine-learning framework for adsorption-energy prediction and regime-level screening on doped TMDs. A final data set of 354 first-principles adsorption entries was constructed for six hazardous gases, four TMD hosts, and substitutional metal dopants using 12 adsorption-configuration-independent descriptors. Among nine regression models, the boosted-tree models GBR and XGB provided the most reliable predictions, with held-out test R 2 values above 0.95. SHAP analysis highlights gas-phase zero-point energy, dopant valence electron count, and molecular dipole moment as influential model-level descriptors. By integrating regression, descriptor-level interpretation, adsorption-regime classification, and holdout validation, this work provides a thermodynamics-guided reference for rapidly screening doped TMD candidates for gas-sensing applications.