XGBoost: An Optimal Machine Learning Model with Just Structural Features to Discover MOF Adsorbents of Xe/Kr

The inert gases Xe and Kr mainly exist in the used nuclear fuel (UNF) with the Xe/Kr ratio of 20:80, which it is difficult to separate. In this work, based on the G-MOFs database, high-throughput computational screening for metal-organic frameworks (MOFs) with high Xe/Kr adsorption selectivity was performed by combining grand canonical Monte Carlo (GCMC) simulations and machine learning (ML) technique for the first time. From the comparison of eight classical ML models, it is found that the XGBoost model with seven structural descriptors has superior accuracy in predicting the adsorption and separation performance of MOFs to Xe/Kr. Compared with energetic or electronic descriptors, structural descriptors are easier to obtain. Note that the determination coefficients R ² of the generalized model for the Xe adsorption and Xe/Kr selectivity are very close to 1, at 0.951 and 0.973, respectively. In addition, 888 and 896 MOFs have been successfully predicted by the XGBoost model among the top 1000 MOFs in adsorption capacity and selectivity by GCMC simulation, respectively. According to the feature engineering of the XGBoost model, it is shown that the density (ρ), porosity (ϕ), pore volume (Vol), and pore limiting diameter (PLD) of MOFs are the key features that affect the Xe/Kr adsorption property. To test the generalization ability of the XGBoost model, we also tried to screen MOF adsorbents on the CO₂/CH₄ mixture, it is found that the prediction performance of XGBoost is also much better than that of the traditional machine learning models although with the unbalanced data. Note that the dimension of features of MOFs is low while the quantity of MOF samples in database is very large, which is suitable for the prediction by model such as XGBoost to search the global minimum of cost function rather than the model involving feature creation. The present study represents the first report using the XGBoost algorithm to discover the MOF adsorbates.