| dc.description.abstract | Metal-organic frameworks (MOFs) are considered highly promising gas adsorbents due to their superior porosity and functionality. To more efficiently screen for the best gas adsorbents, molecular simulation and machine learning can be combined to accelerate the development of potential porous materials. Traditionally, descriptors in machine learning for porous materials mainly rely on the structure and chemical properties of the material. This requires further calculations for each porous material. However, since MOFs are modular structures built by selecting different metal nodes and organic linkers, multiple MOFs share the same organic linkers, allowing the reuse of this information across different porous materials.
This study introduces a new modular descriptor for the surface charge distribution of organic linker, known as the sigma profile. The sigma profile is generated using the conductor-like screening model, which combines solvent thermodynamics and computational quantum mechanics. It focuses on the probability distribution of molecular surface fragments, considering their specific charge densities. This study combines molecular simulation and machine learning to predict the adsorption characteristics of two types of gases, hydrocarbon and acid gases, in MOFs. First, the Henry’s coefficient (KH), which indicates the adsorption strength of hydrocarbon and acid gases in MOFs, is calculated using the Monte Carlo Widom insertion method. Next, two types of machine learning models, random forest (RF) and extreme gradient boosting (XGBoost), are established to predict the adsorption behaviors of methane, ethane, propane, ethylene, carbon dioxide, and hydrogen sulfide in 3180 MOF materials. The most accurate model is identified, and the enhancement of model performance by the sigma profile descriptor is explored. The study further tests the model on hydrocarbons in 100 MOFs composed of two types of organic linkers, demonstrating that the sigma profile significantly improves prediction accuracy. Feature importance analysis confirms the significant role of the sigma profile descriptor. Finally, the impact of reducing the number of sigma profile features on prediction accuracy is investigated, showing that the increase in error is negligible and overall performance remains stable. This indicates that even with fewer sigma profile features, effective predictions can be made with limited computational resources, reaffirming the utility of modular descriptors in MOFs.
Overall, this study presents the sigma profile, a novel modular descriptor for predicting gas adsorption in MOFs using molecular simulation and machine learning. The sigma profile enhances prediction accuracy and efficiency, offering a valuable resource for future research. | en_US |