Zihao Wang

Publication

Journal Articles

  1. Z. Wang, T. Zhou, K. Sundmacher. BayesCAMPD: Data-Efficient and closed-loop integrated molecular and process design using Bayesian optimization. AIChE Journal, 2026. (Full text and GitHub repository)
  2. Z. Wang, T. Zhou. Computer-aided metal-organic framework screening and design approaches toward efficient carbon capture processes. Molecular Systems Design & Engineering, 2025, 10, 1005–1023. (Full text)
  3. Z. Wang, T. Zhou, K. Sundmacher. Data-driven integrated design of solvents and extractive distillation processes. AIChE Journal, 2023, 69(12), e18236. (Full text and GitHub repository)
  4. 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. (Full text and GitHub repository)
  5. 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. (Full text and GitHub repository)
  6. 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. (Full text)
  7. Z. Wang, Z. Song, T. Zhou. Machine learning for ionic liquid toxicity prediction. Processes, 2021, 9(1), 65. (Full text and GitHub repository)
  8. 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. (Full text)
  9. 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. (Full text)
  10. S. Chen, Y. Chen, Z. Zhu, Z. Wang, X. Zhang, Z. Li, W. Du, C. Chang, W. Shen. CIF2MOFNet: A deep learning model with multi-dimensional coordinate features to accelerate MOF screening for CO2 capture. AIChE Journal, 2026.
  11. 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.
  12. Y. Chen, Z. Wang, C. Chang, W. Shen. Active learning-optimized dynamic sampling strategy for enhanced toxicity prediction of chemical compounds. Industrial & Engineering Chemistry Research, 2025, 64(44), 21196–21210.
  13. 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.
  14. 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. (Full text)
  15. 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. (Full text)
  16. 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. (Full text)
  17. 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. (Full text)
  18. 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. (Full text)
  19. 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. (Full text)
  20. 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. (Full text)
  21. S. Zhao, M. Zhang, Z. Wang, X. Xian. Enhanced high-rate performance of Li4Ti5O12 microspheres/multiwalled carbon nanotubes composites prepared by electrostatic self-assembly. Electrochimica Acta, 2018, 276, 73–80. (Full text)
  22. Zhao, M. Zhang, X. Xian, O. Ka, Z. Wang, J. Wang. Insight into the formation mechanism of Li4Ti5O12 microspheres obtained by a CTAB-assisted synthetic method and their electrochemical performances. Journal of Materials Chemistry A, 2017, 5(26), 13740–13747. (Full text)


Conference Papers

  1. Z. Wang, T. Zhou, K. Sundmacher. Molecular property targeting for optimal solvent design in extractive distillation processes. Computer Aided Chemical Engineering, 2023, 1247–1252. (Full text and GitHub repository)
  2. Z. Wang, T. Zhou, K. Sundmacher. A novel machine learning-based optimization approach for the molecular design of solvents. Computer Aided Chemical Engineering, 2022, 1477–1482. (Full text and GitHub repository)
  3. T. Zhou, Z. Wang, K. Sundmacher. A new machine learning framework for efficient MOF discovery: Application to hydrogen storage. Computer Aided Chemical Engineering, 2022, 1807–1812. (Full text)


Conference Talks and Posters

  1. Z. Wang, T. Zhou, K. Sundmacher. Efficient computer-aided molecular and process design driven by Bayesian optimization. Oral Presentation at 12th World Congress of Chemical Engineering and the 21st Asian Pacific Confederation of Chemical Engineering Congress. Beijing, China, July 16, 2025.
  2. Z. Wang, T. Zhou, K. Sundmacher. Data-driven integrated design of solvents and extractive distillation processes. Oral Presentation at 2023 AIChE Annual Meeting. Orlando, USA, November 08, 2023.
  3. Z. Wang, T. Zhou, K. Sundmacher. Molecular property targeting for optimal solvent design in extractive distillation processes. Poster at 33rd European Symposium on Computer-Aided Process Engineering (ESCAPE-33). Athens, Greece, June 19, 2023.
  4. Z. Wang, T. Zhou, K. Sundmacher. A novel machine learning-based optimization approach for the molecular design of solvents. Keynote Oral Presentation at 32nd European Symposium on Computer-Aided Process Engineering (ESCAPE-32). Toulouse, France, June 13, 2022.