## 3 Results and Discussion
### Machine Learning
Figure 4 reveals that RF has the highest correlation coefficient value (0.982), while the support vector machine algorithm has the lowest (0.886). Thus, the prediction accuracy obtained by the RF algorithm is the highest. Therefore, among the 4 ML algorithms, the structure-performance relationship of RF on adsorption selectivity obtains more information, and the prediction results are the best. RF has good generalization ability and strong model learning ability, and this type of ML is suitable for the system. To verify the accuracy of four ML algorithms, we performed 5 repeated predictions, listed in Table S3. The repeated prediction results do not vary significantly, confirming that the RF algorithm is a suitable model. Because RF introduces two kinds of randomness (sampling randomness and feature randomness), it has strong generalization ability. In previous studies, random forest algorithms also exhibited the best prediction results [52; 53]. Whether the overfitting of the model is an important issue. We used combinations of different descriptors and 5 times 5-fold cross-validation to verify the RF model. The results showed that the selected model was not overfitting. During the material screening process, the relative importance of parameters may affect the ultimate screening results. We selected the best RF algorithm to predict the relative importance of each descriptor. The relative importance percentages are shown in Figure 5 and Table S7. We used mean squared error (MSE) to evaluate the relative importance of the 6 descriptors; the greater the percentage of relative importance of the resulting descriptors, the higher the relative contribution of the specific descriptor. According to the results presented in Figure 5, the percentage of \(Q_{\text{st}}\) is the largest, thus indicating that \(Q_{\text{st}}\) exerts the greatest impact on the adsorption selectivity. The relative importance of the MOF descriptors to the adsorption selectivity is \(Q_{\text{st}}>\rho>\text{LCD}>\text{VSA}\geq\phi>\text{PLD}\). The importances of VSA and \(\phi\) are very close, indicating that the effects of two descriptors on adsorption selectivity are roughly equal. From a material science point of view, the larger the \(\phi\), the larger the VSA. This may be the reason why the effects of these parameters are essentially the same. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**ML Algorithms**} & \multicolumn{2}{c}{**R Value**} \\ \cline{2-3}  & **Train** & **Test** \\ \hline BPNN & 0.982 & 0.979 \\ RF & 0.994 & 0.981 \\ DT & 0.985 & 0.969 \\ SVM & 0.915 & 0.886 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The 4 ML algorithms for predicting the correlation coefficient \(R\) value of the adsorption selectivity. Figure 4: The test results of four machine-learning (ML) algorithms predicted versus simulated \(\text{S}_{\text{ads}}\)(CO2/N2+O2) using \(\rho\), \(\phi\), VSA, LCD, PLD and heat of adsorption. (**a**) back propagation neural network (BPNN), (**b**) random forest (RF), (**c**) decision tree (DT), (**d**) support vector machine (SVM). The color of point represents the number of metal-organic frameworks (MOFs), and the unit of number is a base-10 logarithm of MOF numbers. Figure 5: Predicted by the Random Forest, the relative importance of the six descriptors for adsorption selectivity.