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 R2 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.
This work presents a machine learning-symbolic regression strategy to develop a physically interpretable formula for predicting low pressure CO2 adsorption capacity in hypothetical metal-organic frameworks (hMOFs), and proposes a physics-guided expression that enables efficient prediction and provides clearer insight i...
Yimin Shao, Sheng-Ling Ma, Sheng-Hong Ju et al.· 0 citations
A database of 280 experimental records and 20 descriptors was compiled to predict adsorption capacity and interpret descriptor-performance relationships, and the framework supports interpretable prediction and hypothesis generation for gaseous arsenic adsorption by metal oxides.
Yanhong Zhu, Qi Liu, Shuang-Chun Wen et al.· Journal of Environmental Man...· 0 citations
A realistic performance boundary is outlined for bulk-to-surface ML in this benchmark: O* can be very coarsely prioritized from bulk descriptors within a limited domain, whereas H* and OH* are unlikely to be quantitatively predicted from bulk descriptors alone and would benefit from surface-aware models.
Metal–organic frameworks (MOFs) are promising materials for adsorption and separation, but accurately predicting water adsorption remains a major challenge in molecular simulation. Classical force fields, such as UFF, often fail to capture the strong, directional hydrogen-bonding interactions between water and MOFs, wh...
Yu-Tao Li, Xiao-Qi Zhang, Xin Jin et al.· Journal of Chemical Theory a...· 0 citations
Despite the rapid adoption of machine learning (ML) in materials discovery, its application to water contaminant adsorption remains fundamentally constrained. In this study, we systematically evaluated whether current peer-reviewed literature on magnetite nanoparticle (MNPs) adsorbents contains sufficient descriptor...
A. I. Yunus, Jacob Song, Samuel Darko et al.· ACS ES&T Water· 0 citations
Aggregation‐induced emission (AIE) has revolutionized the design of photoluminescent materials by enabling strong solid‐state emission from molecularly nonemissive compounds. However, rational prediction of AIE properties remains challenging because photophysical behavior depends not only on molecular structure but a...
