# Intelligent Prediction of Nitro Oxide Capture in Designable Ionic Liquids
## 4 Results and Discussion
### Solubility Prediction of N\({}_{2}\)O in Ionic Liquids via the Voting Method  
The modeling results obtained via the voting method during the testing stages are presented in Table 5. As shown, the voting method achieved a more satisfactory result. For most of the ionic liquids, the solubility of N\({}_{2}\)O could be accurately predicted with the models developed using the voting technique. In particular, in [BMIM][Tf\({}_{2}\)N], it had a very small MSE value of 3.115 \(\times\) 10\({}^{-5}\), an RMSE of 5.581 \(\times\) 10\({}^{-3}\), an MAE of 4.042 \(\times\) 10\({}^{-3}\), and a high R\({}^{2}\) value of 0.9979. These metrics are acceptable in intelligent model prediction. However, for the particular case of [DMIM][MP], the prediction was not accurate, with an MSE of 2.861 \(\times\) 10\({}^{-3}\), an RMSE of 5.349 \(\times\) 10\({}^{-2}\), an MAE of 4.962 \(\times\) 10\({}^{-2}\), and an R\({}^{2}\) of 0.5697. Different algorithm combinations can avoid this kind of inaccuracy in particular cases. The overall metrics for the training sets, test sets, and all datasets are shown in Table 6. The voting method had an average MSE of 6.582 \(\times\) 10\({}^{-4}\), an average RMSE of 1.873 \(\times\) 10\({}^{-2}\), an average MAE of 1.519 \(\times\) 10\({}^{-2}\), and an average R\({}^{2}\) of 0.9405. This performance was better than that of the linear regression method. Cross plots of the model prediction and experimental values derived via the voting method are shown in Figure 6. For the two ionic liquids we chose to use as examples, [BMIM][Tf\({}_{2}\)N] and [(ETO)\({}_{2}\)IM][Tf\({}_{2}\)N], the predictions of N\({}_{2}\)O solubility closely matched the experimental values in both the training and testing stages. From the visual results, we can also see that the voting method was a reliable method of determining how N\({}_{2}\)O could dissolve in the majority of ionic liquids. This algorithm can utilize the advantages of various algorithms to construct a more accurate model. In our previous research [36], we showed that the voting method also had satisfactory performance in the prediction of CO\({}_{2}\) and H\({}_{2}\)S solubility, which indicates that this algorithm has good generalizability and could be applied to different systems in gas solubility prediction. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Algorithms** & **MSE** & **RMSE** & **MAE** & **R\({}^{2}\)** \\ \hline \multirow{3}{*}{Training sets} & Linear regression & 2.758 \(\times\) 10\({}^{-3}\) & 4.575 \(\times\) 10\({}^{-2}\) & 3.603 \(\times\) 10\({}^{-2}\) & 0.8560 \\  & Voting & 1.319 \(\times\) 10\({}^{-4}\) & 1.043 \(\times\) 10\({}^{-2}\) & 8.072 \(\times\) 10\({}^{-3}\) & 0.9904 \\  & TLFFNN & 2.468 \(\times\) 10\({}^{-5}\) & 3.924 \(\times\) 10\({}^{-3}\) & — & 0.9987 \\ \hline \multirow{3}{*}{Test sets} & Linear regression & 4.037 \(\times\) 10\({}^{-3}\) & 5.459 \(\times\) 10\({}^{-2}\) & 4.266 \(\times\) 10\({}^{-2}\) & 0.7287 \\  & Voting & 1.185 \(\times\) 10\({}^{-3}\) & 2.702 \(\times\) 10\({}^{-2}\) & 2.231 \(\times\) 10\({}^{-2}\) & 0.8907 \\  & TLFFNN & 2.268 \(\times\) 10\({}^{-5}\) & 4.543 \(\times\) 10\({}^{-3}\) & — & 0.9981 \\ \hline \multirow{3}{*}{All datasets} & Linear regression & 3.397 \(\times\) 10\({}^{-3}\) & 5.017 \(\times\) 10\({}^{-2}\) & 3.934 \(\times\) 10\({}^{-2}\) & 0.7924 \\  & Voting & 6.582 \(\times\) 10\({}^{-4}\) & 1.873 \(\times\) 10\({}^{-2}\) & 1.519 \(\times\) 10\({}^{-2}\) & 0.9405 \\ \cline{1-1}  & TLFFNN & 2.368 \(\times\) 10\({}^{-5}\) & 4.234 \(\times\) 10\({}^{-3}\) & — & 0.9984 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Average performance of the models.