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王梓豪，工学博士，助理研究员。2025年博士毕业于德国马克斯普朗克复杂技术系统动力学研究所，2017年和2020年分别本科和硕士毕业于重庆大学。主要从事数据驱动的分子设计、材料筛选、化工过程建模与优化等方面的研究。
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联系方式
E-mail：zwang1995@cqu.edu.cn Tel：18225278255
研究方向
面向化学工业数字化与智能化，开发数据驱动的多尺度建模与优化方法，实现对有机溶剂、功能材料以及复杂化工分离系统的高效开发与优化设计。
代表性论文、专著和专利
[1] Z. Wang, T. Zhou, K. Sundmacher. Data-driven integrated design of solvents and extractive distillation processes. AIChE Journal, 2023, 69(12), e18236.
[2] 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.
[3] 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.
[4] 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.
[5] 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.
[6] 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.
[7] Z. Wang, Z. Song, T. Zhou. Machine learning for ionic liquid toxicity prediction. Processes, 2021, 9(1), 65.
[8] H. Qin, Z. Wang, J. Ruan, F. Wei, Z. Yuan, W. Jiao, G. Qi, Y. Liu. Integrating machine learning model and computer-aided molecular design toward rational ionic liquid selection for separating fluorinated refrigerants. Separation and Purification Technology. 2024, 356, 129796.
[9] 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.
[10] H. Qin, Z. Wang, Z. Song, X. Zhang, T. Zhou. High-throughput computational screening of ionic liquids for butadiene and butene separation. Processes, 2022, 10(1), 165.
[11] H. Wen, Y. Su, Z. Wang, S. Jin, J. Ren, W. Shen, M. Eden. A systematic modeling methodology of deep neural network-based structure-property relationship for rapid and reliable prediction on flashpoints. AIChE Journal, 2022, 68(1), e17402.
[12] X. Zhang, S. Sethi, Z. Wang, T. Zhou, Z. Qi, K. Sundmacher. A neural recommender system for efficient adsorbent screening. Chemical Engineering Science, 2022, 259, 117801.
[13] H. Qin, Z. Wang, T. Zhou, Z. Song. Comprehensive evaluation of COSMO-RS for predicting ternary and binary ionic liquid-containing vapor-liquid equilibria. Industrial & Engineering Chemistry Research, 2021, 60(48), 17761-17777.
[14] A. Yang, Y. Su, Z. Wang, S. Jin, J. Ren, X. Zhang, W. Shen, J.H. Clark. A multi-task deep learning neural network for predicting flammability-related properties from molecular structures. Green Chemistry, 2021, 23(12), 4451-4465.
[15] Y. Su, Z. Wang, S. Jin, W. Shen, J. Ren, M.R. Eden. An architecture of deep learning in QSPR modeling for the prediction of critical properties using molecular signatures. AIChE Journal, 2019, 65(9), e16678.
团队仪器设备
