Exploring the chemical space of ionic liquids for CO2 dissolution through generative machine learning models
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 solubili...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co. Ltd.
2025-09-01
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| Series: | Green Chemical Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666952824000414 |
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| author | Xiuxian Chen Guzhong Chen Kunchi Xie Jie Cheng Jiahui Chen Zhen Song Zhiwen Qi |
| author_facet | Xiuxian Chen Guzhong Chen Kunchi Xie Jie Cheng Jiahui Chen Zhen Song Zhiwen Qi |
| author_sort | Xiuxian Chen |
| collection | DOAJ |
| description | 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 t-distributed 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. |
| format | Article |
| id | doaj-art-ff42740a19f14b59a74b18f12f6c3097 |
| institution | Kabale University |
| issn | 2666-9528 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | KeAi Communications Co. Ltd. |
| record_format | Article |
| series | Green Chemical Engineering |
| spelling | doaj-art-ff42740a19f14b59a74b18f12f6c30972025-08-20T03:48:18ZengKeAi Communications Co. Ltd.Green Chemical Engineering2666-95282025-09-016333534310.1016/j.gce.2024.06.005Exploring the chemical space of ionic liquids for CO2 dissolution through generative machine learning modelsXiuxian Chen0Guzhong Chen1Kunchi Xie2Jie Cheng3Jiahui Chen4Zhen Song5Zhiwen Qi6State Key Laboratory of Chemical Engineering, School of Chemical Engineering, East China University of Science and Technology, Shanghai, 200237, ChinaState Key Laboratory of Chemical Engineering, School of Chemical Engineering, East China University of Science and Technology, Shanghai, 200237, ChinaState Key Laboratory of Chemical Engineering, School of Chemical Engineering, East China University of Science and Technology, Shanghai, 200237, ChinaState Key Laboratory of Chemical Engineering, School of Chemical Engineering, East China University of Science and Technology, Shanghai, 200237, ChinaState Key Laboratory of Chemical Engineering, School of Chemical Engineering, East China University of Science and Technology, Shanghai, 200237, ChinaCorresponding author.; State Key Laboratory of Chemical Engineering, School of Chemical Engineering, East China University of Science and Technology, Shanghai, 200237, ChinaCorresponding author.; State Key Laboratory of Chemical Engineering, School of Chemical Engineering, East China University of Science and Technology, Shanghai, 200237, ChinaFor 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 t-distributed 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.http://www.sciencedirect.com/science/article/pii/S2666952824000414Ionic liquidsCO2 solubilityVariational autoencoderParticle swarm optimizationChemical space exploration |
| spellingShingle | Xiuxian Chen Guzhong Chen Kunchi Xie Jie Cheng Jiahui Chen Zhen Song Zhiwen Qi Exploring the chemical space of ionic liquids for CO2 dissolution through generative machine learning models Green Chemical Engineering Ionic liquids CO2 solubility Variational autoencoder Particle swarm optimization Chemical space exploration |
| title | Exploring the chemical space of ionic liquids for CO2 dissolution through generative machine learning models |
| title_full | Exploring the chemical space of ionic liquids for CO2 dissolution through generative machine learning models |
| title_fullStr | Exploring the chemical space of ionic liquids for CO2 dissolution through generative machine learning models |
| title_full_unstemmed | Exploring the chemical space of ionic liquids for CO2 dissolution through generative machine learning models |
| title_short | Exploring the chemical space of ionic liquids for CO2 dissolution through generative machine learning models |
| title_sort | exploring the chemical space of ionic liquids for co2 dissolution through generative machine learning models |
| topic | Ionic liquids CO2 solubility Variational autoencoder Particle swarm optimization Chemical space exploration |
| url | http://www.sciencedirect.com/science/article/pii/S2666952824000414 |
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