文献类型：期刊文献

英文题名：Exploring the chemical space of ionic liquids for CO2 dissolution through generative machine learning models

作者：Chen, Xiuxian[1];Chen, Guzhong[1];Xie, Kunchi[1];Cheng, Jie[1];Chen, Jiahui[1];Song, Zhen[1];Qi, Zhiwen[1]

机构：[1]East China Univ Sci & Technol, Sch Chem Engn, State Key Lab Chem Engn, Shanghai 200237, Peoples R China

年份：2025

卷号：6

期号：3

起止页码：335

外文期刊名：GREEN CHEMICAL ENGINEERING

收录：EI(收录号：20243016742458);WOS:【ESCI(收录号:WOS:001515038500001)】；

基金：This research is supported by the National Natural Science Founda-tion of China (NSFC) under the grants Nos. 22278134, 22208098, and 21CAA01709.

语种：英文

外文关键词：Ionic liquids; Variational autoencoder; Particle swarm optimization; Chemical space exploration; CO2 solubility

摘要：For discovering uncharted chemical space of ionic liquids (ILs) for CO2 dissolution, a reliable generative framework combining re-balanced variational autoencoder (VAE), artificial neural network (ANN), and particle swarm optimization (PSO) is developed based on a comprehensive experimental solubility database from literature. The re-balanced VAE transforms the chemical space of ILs into continuous latent space, which is demonstrated by tdistributed stochastic neighbor embedding (t-SNE) visualization and sampled ions of the latent space. ANN is connected with the re-balanced VAE to predict the CO2 solubility and the resultant VAE-ANN model achieves a low mean absolute error (MAE) of 0.022 on the test set. Lastly, the PSO algorithm is employed to search the latent space for optimal IL structures with the highest predicted solubility. A total of 5120 ILs are generated and optimized through 10 parallel runs of PSO. Their CO2 solubilities are predicted and compared to those of the 3735 ILs combined with the already-known cations and anions in the CO2 solubility database under 298.15 K and 100 kPa. The results demonstrate a notably larger distribution of higher CO2 solubility in optimized ILs after PSO, which effectively points out the significance and directions for exploring the wide IL chemical space.