Intelligent prediction of ionic liquids and deep eutectic solvents by machine learning

doi:10.1016/j.cjche.2025.06.006

Chinese Journal of Chemical Engineering ›› 2025, Vol. 84 ›› Issue (8): 227-243.DOI: 10.1016/j.cjche.2025.06.006

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Intelligent prediction of ionic liquids and deep eutectic solvents by machine learning

Yuan Tian¹, Honghua Zhang², Yueyang Qiao², Han Yang², Yanrong Liu^1,2, Xiaoyan Ji³

1. CAS Key Laboratory of Green Process and Engineering, State Key Laboratory of Mesoscience and Engineering, Beijing Key Laboratory of Ionic Liquids Clean Process, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China;
2. Longzihu New Energy Laboratory, Zhengzhou Institute of Emerging Industrial Technology, Henan University, Zhengzhou 450000, China;
3. Energy Engineering, Division of Energy Science, Lule? University of Technology, Lule? 97187, Sweden

Received:2025-02-28 Revised:2025-05-29 Accepted:2025-06-11 Online:2025-06-18 Published:2025-08-28
Contact: Yanrong Liu,E-mail:yrliu@ipe.ac.cn;Xiaoyan Ji,E-mail:xiaoyan.ji@ltu.se
Supported by:
This work was supported by the National Key Research and Development Program of China (2022YFB3504702). X. Ji is thankful for the financial support from Horizon-EIC, Pathfinder challenges (101070976); Yanrong Liu also thankful the financial support from the National Natural Science Foundation of China (22278402, 22478389), the Key Research and Development Program of Henan Province (231111241800), State Key Laboratory of Mesoscience and Engineering (MESO-23-A08), and the Frontier Basic Research Projects of Institute of Process Engineering, CAS (QYJC-2023-03).

Intelligent prediction of ionic liquids and deep eutectic solvents by machine learning

Yuan Tian¹, Honghua Zhang², Yueyang Qiao², Han Yang², Yanrong Liu^1,2, Xiaoyan Ji³

1. CAS Key Laboratory of Green Process and Engineering, State Key Laboratory of Mesoscience and Engineering, Beijing Key Laboratory of Ionic Liquids Clean Process, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China;
2. Longzihu New Energy Laboratory, Zhengzhou Institute of Emerging Industrial Technology, Henan University, Zhengzhou 450000, China;
3. Energy Engineering, Division of Energy Science, Lule? University of Technology, Lule? 97187, Sweden

通讯作者: Yanrong Liu,E-mail:yrliu@ipe.ac.cn;Xiaoyan Ji,E-mail:xiaoyan.ji@ltu.se
基金资助:
This work was supported by the National Key Research and Development Program of China (2022YFB3504702). X. Ji is thankful for the financial support from Horizon-EIC, Pathfinder challenges (101070976); Yanrong Liu also thankful the financial support from the National Natural Science Foundation of China (22278402, 22478389), the Key Research and Development Program of Henan Province (231111241800), State Key Laboratory of Mesoscience and Engineering (MESO-23-A08), and the Frontier Basic Research Projects of Institute of Process Engineering, CAS (QYJC-2023-03).

Abstract

Abstract: Ionic liquids (ILs) and deep eutectic solvents (DESs) as green solvents have attracted dramatic attention recently due to their highly tunable properties. However, traditional experimental screening methods are inefficient and resource-intensive. The article provides a comprehensive overview of various ML algorithms, including artificial neural network (ANN), support vector machine (SVM), random forest (RF), and gradient boosting trees (GBT), etc., which have demonstrated exceptional performance in handling complex and high-dimensional data. Furthermore, the integration of ML with quantum chemical calculations and conductor-like screening model-real solvent (COSMO-RS) has significantly enhanced predictive accuracy, enabling the rapid screening and design of novel solvents. Besides, recent ML applications in the prediction and design of ILs and DESs focused on solubility, melting point, electrical conductivity, and other physicochemical properties become more and more. This paper emphasizes the potential of ML in solvent design, overviewing an efficient approach to accelerate the development of sustainable and high-performance materials, providing guidance for their widespread application in a variety of industrial processes.

Key words: Intelligent prediction, Ionic liquids, Deep eutectic solvents, Machine learning

摘要： Ionic liquids (ILs) and deep eutectic solvents (DESs) as green solvents have attracted dramatic attention recently due to their highly tunable properties. However, traditional experimental screening methods are inefficient and resource-intensive. The article provides a comprehensive overview of various ML algorithms, including artificial neural network (ANN), support vector machine (SVM), random forest (RF), and gradient boosting trees (GBT), etc., which have demonstrated exceptional performance in handling complex and high-dimensional data. Furthermore, the integration of ML with quantum chemical calculations and conductor-like screening model-real solvent (COSMO-RS) has significantly enhanced predictive accuracy, enabling the rapid screening and design of novel solvents. Besides, recent ML applications in the prediction and design of ILs and DESs focused on solubility, melting point, electrical conductivity, and other physicochemical properties become more and more. This paper emphasizes the potential of ML in solvent design, overviewing an efficient approach to accelerate the development of sustainable and high-performance materials, providing guidance for their widespread application in a variety of industrial processes.

关键词: Intelligent prediction, Ionic liquids, Deep eutectic solvents, Machine learning

Yuan Tian, Honghua Zhang, Yueyang Qiao, Han Yang, Yanrong Liu, Xiaoyan Ji. Intelligent prediction of ionic liquids and deep eutectic solvents by machine learning[J]. Chinese Journal of Chemical Engineering, 2025, 84(8): 227-243.

Yuan Tian, Honghua Zhang, Yueyang Qiao, Han Yang, Yanrong Liu, Xiaoyan Ji. Intelligent prediction of ionic liquids and deep eutectic solvents by machine learning[J]. 中国化学工程学报, 2025, 84(8): 227-243.

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Intelligent prediction of ionic liquids and deep eutectic solvents by machine learning

Intelligent prediction of ionic liquids and deep eutectic solvents by machine learning

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