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王梓豪，工学博士，助理研究员，弘深青年教师。主要从事化工分离系统智能优化与节能降碳研究，面向化学工业数字化与智能化，开发数据驱动多尺度建模与优化方法，实现有机溶剂、功能材料以及复杂化工分离系统的高效开发与优化设计。已发表SCI论文19篇，其中以第一作者身份在AIChE Journal、Chemical Engineering Science等化工领域主流学术期刊发表论文8篇。
2025.04- 重庆大学，化学化工学院，助理研究员 2020.10-2025.03 德国马克斯普朗克复杂技术系统动力学研究所、马格德堡大学，过程系统工程，博士（导师：Kai Sundmacher教授，合作导师：周腾教授） 2017.09-2020.06 重庆大学，化学化工学院，化学工程与技术，硕士（导师：申威峰教授） 2013.09-2017.06 重庆大学，化学化工学院，化学工程与工艺，学士
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联系方式
邮箱：zwang1995@cqu.edu.cn
研究方向
分子表征学习与构效关系建模
数据驱动材料性能预测与筛选
分离系统跨尺度协同智能优化
代表性论文、专著和专利
代表性论文：
[1] Z. Wang, T. Zhou*. Computer-aided metal-organic framework screening and design approaches toward efficient carbon capture processes. Molecular Systems Design & Engineering, 2025.
[2] Z. Wang, T. Zhou*, K. Sundmacher*. Data-driven integrated design of solvents and extractive distillation processes. AIChE Journal, 2023, 69(12), e18236.
[3] Z. Wang, T. Zhou*, K. Sundmacher. Interpretable machine learning for accelerating the discovery of metal-organic frameworks for ethane/ethylene separation. Chemical Engineering Journal, 2022, 444, 136651.
[4] Z. Wang, Y. Zhou, T. Zhou*, K. Sundmacher. Identification of optimal metal-organic frameworks by machine learning: Structure decomposition, feature integration, and predictive modeling. Computers & Chemical Engineering, 2022, 160, 107739.
[5] Z. Wang, H. Wen, Y. Su, W. Shen*, J. Ren, Y. Ma, J. Li. Insights into ensemble learning-based data-driven model for safety-related property of chemical substances. Chemical Engineering Science, 2022, 248, 117219.
[6] Z. Wang, Y. Su, S. Jin, W. Shen*, J. Ren, X. Zhang, J.H. Clark. A novel unambiguous strategy of molecular feature extraction in machine learning assisted predictive models for environmental properties. Green Chemistry, 2020, 22(12), 3867-3876.
[7] Z. Wang, Y. Su, W. Shen*, S. Jin*, J.H. Clark, J. Ren, X. Zhang. Predictive deep learning models for environmental properties: the direct calculation of octanol-water partition coefficients from molecular graphs. Green Chemistry, 2019, 21(16), 4555-4565.
[8] Z. Wang, Z. Song, T. Zhou*. Machine learning for ionic liquid toxicity prediction. Processes, 2021, 9(1), 65.
[9] S. Zhang, Z. Wang, H. Gao, T. Zhou*. A multitask learning model for predicting various types of metal-organic framework stability. Industrial & Engineering Chemistry Research, 2025, 64(29), 14576-14589.
[10] T. Zhou*, C. Gui, L. Sun, Y. Hu, H. Lyu, Z. Wang, Z. Song, G. Yu. Energy applications of ionic liquids: Recent developments and future prospects. Chemical Reviews, 2023, 123(21), 12170-12253.
团队仪器设备
