# Intelligent Prediction of Nitro Oxide Capture in Designable Ionic Liquids
## 1 Introduction  
Nitrous oxide (N\({}_{2}\)O) is a greenhouse gas which can cause global warming. Although the concentration of N\({}_{2}\)O in the atmosphere is lower than that of CO\({}_{2}\), N\({}_{2}\)O has a 298-fold higher single-molecule warming potential than carbon dioxide (CO\({}_{2}\)). N\({}_{2}\)O can also damage the ozone layer, cause the ozone hole to expand, and expose human beings to solar ultraviolet radiation [1]. The harm caused by N\({}_{2}\)O concerns researchers [2; 3; 4]. Different methods are used to capture and remove N\({}_{2}\)O from waste gases [5; 6], among which the application of a suitable green solvent is crucial [7; 8]. Ionic liquids (ILs) are green solvents which are widely applied in waste gas removal. Much research [9; 10; 11; 12] has been carried out on gas removal using ionic liquids, such as CO\({}_{2}\) and H\({}_{2}\)S. The solubilities of gases in ionic liquids [13; 14; 15; 16; 17] can be determined via experimental analysis. However, laboratory-based methods are usually associated with high costs and toxic exposure. In addition, theoretically, there could be up to 1 trillion types of ionic liquids, meaning that laboratory analyses are not comprehensive enough. A computer model is required for ionic liquid screening. In recent years, machine learning has proved promising in applications related to the environment and chemistry [18; 19; 20; 21; 22; 23]. [PERSON] et al. [24] utilized a least square support vector machine (LSSVM) model to accurately estimate the efficiency of chemical flooding in an oil reservoir. [PERSON] et al. [25] applied an artificial-neural-network-based (ANN-based) model to predict the performance of CO\({}_{2}\)-foam flooding on a laboratory scale, aiming to improve the oil recovery. In a study by [PERSON] et al. [26], an artificial neural network was used to forecast the capture of CO\({}_{2}\) and H\({}_{2}\)S in 32 distinct types of ionic liquids. A \"single decision tree\" and an \"ensemblerandom forest\" were utilized by [PERSON] et al. [27] to forecast the solubility of CO\({}_{2}\) in various ionic liquids. Numerous studies using machine learning have demonstrated precise predictions of CO\({}_{2}\) and H\({}_{2}\)S capture in various ionic liquids [26; 27; 28; 29; 30; 31; 32]. Compared with CO\({}_{2}\), much less modeling work has been carried out on N\({}_{2}\)O capture using ionic liquids. To estimate the solubility of N\({}_{2}\)O in various ionic liquids, [PERSON] et al. [33] employed three machine learning algorithms; they found that the predictions derived from machine learning models were consistent with laboratory-based results. The support vector machine (SVM) method, in particular, performed better than the other two models. Four artificial intelligence (AI) strategies were also utilized by [PERSON] et al. [34] to forecast the solubility of acid gases in various ionic liquids. With an absolute relative deviation of 8.7%, the coupled simulated annealing-least squares support vector machine technique was shown to perform the best. In a study performed by [PERSON] et al. [35], the solubility of N\({}_{2}\)O in 13 different types of ionic liquids was predicted using three intelligent computing methods. They discovered that the cascaded forward neural network (CFNN) model achieved the highest accuracy. Modeling work on N\({}_{2}\)O is still scarce; therefore, which model is superior? Are the models suitable for CO\({}_{2}\) and H\({}_{2}\)S suitable for N\({}_{2}\)O? More work needs to be carried out to validate the performance of machine learning in the prediction of N\({}_{2}\)O capture in ionic liquids. In this study, the solubility of N\({}_{2}\)O gas in seven different kinds of ionic liquids was modeled based on three machine learning methods. In our previous study [36], the solubility of CO\({}_{2}\) and H\({}_{2}\)S in ionic liquids was predicted using twelve machine learning techniques.