Chapter 3
Machine Learning-Aided Rational Screening of Task-Specific Ionic Liquids
Ruofan Gu,
Zhen Song,
Ruofan Gu
State Key Laboratory of Chemical Engineering, School of Chemical Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237 China
Search for more papers by this authorZhen Song
State Key Laboratory of Chemical Engineering, School of Chemical Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237 China
Search for more papers by this authorRuofan Gu,
Zhen Song,
Ruofan Gu
State Key Laboratory of Chemical Engineering, School of Chemical Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237 China
Search for more papers by this authorZhen Song
State Key Laboratory of Chemical Engineering, School of Chemical Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237 China
Search for more papers by this authorBook Editor(s):Chang He, Jingzheng Ren,
Jingzheng Ren
Search for more papers by this authorSummary
Ionic liquids (ILs) are renowned for their unique physicochemical properties, flexible structural designability, and expansive chemical diversity. These attributes make ILs highly promising for a wide range of chemical processes, as they can be tailored to meet specific target properties. The fundamental challenge, however, lies in the efficient and reliable customization of the appropriate IL for designated applications. In this context, computer-aided molecular design (CAMD) methodologies have gained prominence for the rational screening of ILs, which is also commonly referred to as computer-aided ionic liquid design (CAILD). Based on the previous contributions of our group to this field, this chapter tries to offer a mini review of the use of machine learning to facilitate the rational selection of ILs. Within the context of CAILD, this chapter delves into critical elements concerning both forward structure–property modeling and the reverse molecular design of ILs. The forward modeling task includes diverse molecular representations of ILs and their associated representative models, particularly focusing on thermodynamic properties. Correspondingly, the reverse molecular design section summarizes efforts in establishing various molecular design frameworks. Finally, some insights into future directions are also offered.
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