## 3 Results and Discussion
### Machine Learning  
Currently, machine learning has been used to predict the performance of materials and to filter high-performance materials from large databases [51]. Aiming to discover a better machine prediction method suitable for this system, we individually compared the simulations of the 4 ML algorithms commonly used in big data analysis, i.e., the BPNN, DT, RF, and SVM. Among them, BPNN is a kind of forward signal propagation with error back propagation in which the gradient descent algorithm continuously adjusts the weight and threshold until the error is less than a set threshold. Some parameters of BPNN were set: the training function is Levenberg-Marquardt, the transfer function is a hyperbolic tangent sigmoid transfer function, and the performance evaluation function is the mean square error (MSE). The number of hidden layer neurons was 18, the maximum number of training was 1000, the training required an accuracy of 0.001, and the learning rate was 0.01. DT is a traditional method for data classification and screening. Under the condition that the probability of occurrence takes place in various situations, probability analysis is employed to analyze data with the dendritic model to obtain the expected values. The random forest algorithm is composed of multiple decision trees. The setting parameters of DT were: standard CART (classification and regression tree) used to select the best split predictor at each node. The criteria of splitting and pruning are the MSE function. After optimizing and pruning, the minimum number of branch node observations was 10, the minimum number of leaf node observations was 4, and the maximal number of decision splits was 1. RF uses the method of randomly selecting split attribute sets to construct a decision tree. The parameters for RF were set as: number of trees 200, minimum leaf size 10. The number of variables  
Figure 3: Henry coefficient \(K_{\text{N2}}\)_versus_ (**a**) \(\phi\) and (**b**) LCD. randomly selected in the variable subset of the node split in each tree was 2. SVM is an algorithm for binary classification of data through supervised learning, and employs mathematical transformation methods to divide data with a certain centralized structure into rules. We chose the support vector machine regression model of Statistics and Machine Learning Toolbox in MATLAB 2016b to predict, where the kernel function is radial basis function (Gaussian), the kernel scale parameter is set as \"auto\", and the loss function is epsilon-insensitive. The box constraint (also called the penalty coefficient, C) was 0.0567, and the half width of epsilon-insensitive band (\(\varepsilon\)) was set as 0.0057. In the radial basis kernel function, Gamma = 1/(2\(\sigma^{2}\)), where \(\sigma\) is the parameter of the kernel function, which can affect the complexity of the SVM regression algorithm. In our study, the value of gamma was 7.125. The solver of convex quadratic programming is sequential minimal optimization (SMO). Before training and testing, we first processed the data set out-of-order, and then randomly divide it into training and testing sets based on a ratio of 7:3. More detailed descriptions of ML algorithms are listed in the supporting information, and the corresponding diagrams of each algorithm are shown in Figures S2-S5. We used BPNN, RF, DT, and SVM to predict the adsorption selectivity, and took the logarithm of the adsorption selectivity in order to reduce the differences associated with the varying magnitudes of the data. The 4 ML for predicting the correlation coefficient \(R\) value of the adsorption selectivity are listed in Table 1 The results of the testing and training are shown in Figure 4 and Figure S12. The distribution trends of the points in Figure 4a-d are all straight lines inclined upward. The different colors from top to bottom in the figure represent an increase in the number of points, and most of the points are concentrated on the diagonal, indicating that the prediction results are good.