论文著作：
1. Song Z#,*, Chen JH#, Cheng J, Chen GZ, Qi ZW*. Computer-Aided molecular design of ionic liquids as advanced process media: A review from fundamentals to applications. Chemical Reviews, 2024, 124(2): 248-317.
2. Xie K, Chen J, Cheng J, Wang R, Cheng H, Qi ZW*, Zhu KK, Song Z*. Enhancing aromatics extraction by double salt ionic liquids: Rational screening‐validation and mechanistic insights. AIChE Journal, 2024, 70(2): e18301.
3. Fang Y, Fan Y, Xie KC, Ge WX, Zhu YH, Qi ZW, Song Z*, Jiang HL*, Li CZ*. Boosting hydrogen peroxide electrosynthesis via modulating the interfacial hydrogen‐bond environment. Angewandte Chemie International Edition, 2023, 62(27): e202304413.
4. Chen GZ, Song Z*, Qi ZW*, Sundmacher K. A scalable and integrated machine learning framework for molecular properties prediction. AIChE Journal, 2023, 69(10): e18185.
5. Chen GZ, Song Z*, Qi ZW*, Sundmacher K. Generalizing property prediction of ionic liquids from limited labeled data: A one-stop framework empowered by transfer learning. Digital Discovery, 2023, 2(3): 591-601.
6. Cheng J, Xie K, Guo P, Qin H, Deng L, Qi Z*, Song Z*. Capturing CO2 by ionic liquids and deep eutectic solvents: A comparative study based on multi-level absorbent screening. Chemical Engineering Science, 2023, 281: 119133.
7. Qin H, Xie K, Li L, Cheng J, Song Z*. Enhancing R410A blend separation by using ionic liquids: From UNIFAC model extension, solvent design to molecular dynamics simulation. Chemical Engineering Science, 2023, 274: 118709.
8. Chen JH, Zhu F, Qin H, Song Z*, Qi Z, Sundmacher K*. Rational eutectic solvent design by linking regular solution theory with QSAR modelling. Chemical Engineering Science, 2022, 262: 118042.
9. Chen GZ, Song Z*, Qi Z*, Sundmacher K. Neural recommender system for the activity coefficient prediction and UNIFAC model extension of ionic liquid–solute systems. AIChE Journal, 2021, 67(4): e17171.
10. Song Z, Zhou T, Qi ZW, Sundmacher K. Extending the UNIFAC model for ionic liquid–solute systems by combining experimental and computational databases. AIChE Journal, 2020, 66(2): e16821.
宋震
上海市
研究领域：
储能
研究方向：
围绕介质强化过程开展难分离体系、受限反应体系、储能体系、材料制备体系的应用基础研究，研究兴趣包括：1. 计算机辅助介质分子设计（常规介质、离子液体、低共熔溶剂等）2. 机理与大数据融合的智能建模3. 化工大数据库智能构建与软件平台开发欢迎对机器学习建模、新型介质筛选设计、过程强化等相关模拟与实验方向感兴趣的本科生、研究生、博士生、博士后联系加入！
所在单位：
华东理工大学
