Intelligent Prediction of Nitrous Oxide Capture in Designable Ionic Liquids

Abstract

As a greenhouse gas, nitrous oxide (N2O) is increasingly damaging the atmosphere and environment, and the capture of N2O using ionic liquids (ILs) has recently attracted wide attention. Machine learning can be utilized to rapidly screen ILs suitable for N2O removal. In this study, intelligent predictions of nitrous oxide capture in designable ionic liquids are proposed based on a series of machine learning methods, including linear regression, voting, and a two-layer feed-forward neural network (TLFFNN). The voting model can utilize various algorithms and is highly generalizable to various systems. The TLFFNN model produced the most accurate prediction, with an MSE of 0.00002 and R2 of 0.9981 on test sets. The acceptable performance of the TLFFNN model demonstrates its utility as an accurate and promising candidate model for the prediction of N2O solubility in ILs over other intelligent models. Based on the analysis of the thermodynamic and molecular properties of ionic liquids, in the low-pressure zone, components of [(OH)2IM] and [AC] perform best in capturing N2O, while in the high-pressure zone, components of [(ETO)2IM] and [SCN] are best. This finding will provide new chemical insights for the industrial synthesis of ionic liquids in capturing N2O.

Prediction and Interpretability of Melting Points of Ionic Liquids Using Graph Neural Networks

“…Machine learning (ML) techniques show promise for accurately and effectively predicting the properties of chemical compounds. ,− In different fields, ML methods , are as accurate as traditional simulation techniques such as MD but require less computing power. , Molecular descriptor–based ML (DBML) models have been used to forecast the MPs of ILs. , Molecular descriptors are the numerical values that feature the molecular structures of the ILs. One of the earlier descriptors used group contribution, , which split molecules into fragments; the MP of an IL is calculated by summing the contribution of each fragment.…”

“…Machine learning (ML) techniques show promise for accurately and effectively predicting the properties of chemical compounds. 9 , 16 − 20 In different fields, ML methods 8 , 21 are as accurate as traditional simulation techniques such as MD but require less computing power. 14 , 22 Molecular descriptor–based ML (DBML) models have been used to forecast the MPs of ILs.…”