Publications of Zihao Wang

Qin, H., Wang, Z., Ruan, J., Wei, F., Yuan, Z., Jiao, W., Qi, G., & Liu, Y. (2025)Integrating machine learning model and computer-aided molecular design toward rational ionic liquid selection for separating fluorinated refrigerantsSeparation and Purification Technology, 356 (Part A), 129796doi: 10.1016/j.seppur.2024.129796.

Wang, Z., Zhou, T., & Sundmacher, K. (2023)Data‐driven integrated design of solvents and extractive distillation processesAIChE Journal, 69 (12), e18236doi: 10.1002/aic.18236.

Zhou, T., Gui, C., Sun, L., Hu, Y., Lyu, H., Wang, Z., Song, Z., & Yu, G. (2023)Energy Applications of Ionic Liquids: Recent Developments and Future ProspectsChemical Reviews, 123, 12170–12253doi: 10.1021/acs.chemrev.3c00391.

Qin, H., Wang, Z., Song, Z., Zhang, X., & Zhou, T. (2022)High-Throughput Computational Screening of Ionic Liquids for Butadiene and Butene SeparationProcesses, 10 (1), 165doi: 10.3390/pr10010165.

Wang, Z., Zhou, T., & Sundmacher, K. (2022)Interpretable machine learning for accelerating the discovery of metal-organic frameworks for ethane/ethylene separationChemical Engineering Journal, 444, 136651doi: 10.1016/j.cej.2022.136651.

Wang, Z., Zhou, Y., Zhou, T., & Sundmacher, K. (2022)Identification of Optimal Metal-Organic Frameworks by Machine Learning: Structure Decomposition, Feature Integration, and Predictive ModelingComputers & Chemical Engineering, 160, 107739doi: 10.1016/j.compchemeng.2022.107739.

Wen, H., Su, Y., Wang, Z., Jin, S., Ren, J., Shen, W., & Eden, M. (2022)A systematic modeling methodology of deep neural network‐based structure‐property relationship for rapid and reliable prediction on flashpointsAIChE Journal, 68 (1), 17402doi: 10.1002/aic.17402.

Zhang, X., Sethi, S., Wang, Z., Zhou, T., Qi, Z., & Sundmacher, K. (2022)A neural recommender system for efficient adsorbent screeningChemical Engineering Science, 259, 117801doi: 10.1016/j.ces.2022.117801.

Qin, H., Wang, Z., Zhou, T., & Song, Z. (2021)Comprehensive Evaluation of COSMO-RS for Predicting Ternary and Binary Ionic Liquid-Containing Vapor–Liquid EquilibriaIndustrial & Engineering Chemistry Research, 60 (48), 17761–17777doi: 10.1021/acs.iecr.1c03940.

Wang, Z., Song, Z., & Zhou, T. (2021)Machine Learning for Ionic Liquid Toxicity PredictionProcesses, 9 (1), 65doi: 10.3390/pr9010065.

Wang, Z., Zhou, T., & Sundmacher, K. (2023)Molecular Property Targeting for Optimal Solvent Design in Extractive Distillation ProcessesIn: 33rd European Symposium on Computer Aided Process Engineering: Computer Aided Chemical Engineering, 1247–1252doi: 10.1016/B978-0-443-15274-0.50199-2.

Wang, Z., Zhou, T., & Sundmacher, K. (2022)A Novel Machine Learning-Based Optimization Approach for the Molecular Design of SolventsIn: 32nd European Symposium on Computer Aided Process Engineering, 1477–1482doi: 10.1016/B978-0-323-95879-0.50247-2.

Zhou, T., Wang, Z., & Sundmacher, K. (2022)A New Machine Learning Framework for Efficient MOF Discovery: Application to Hydrogen StorageIn: 14th International Symposium on Process Systems Engineering: 1807–1812doi: 10.1016/B978-0-323-85159-6.50301-8.

Wang, Z., Zhou, T., & Sundmacher, K.Data-driven integrated design of solvents and extractive distillation processesPresented at: 2023 AIChE Annual MeetingOrlando, USA, November 5, 2023.

Wang, Z., Zhou, T., & Sundmacher, K.A Novel Machine Learning-Based Optimization Approach for the Molecular Design of SolventsPresented at: ESCAPE-32Toulouse, France, June 12, 2022.

Wang, Z., Zhou, T., & Sundmacher, K.Molecular Property Targeting for Optimal Solvent Design in Extractive Distillation ProcessesPresented at: ESCAPE-33Athens, Greece, June 18, 2023.

Ayaz, R. M. Z., Wang, Z., Lieb , A., Sundmacher, K., & Scheffler, F.Synthesis and Characterisation of CALF-20/GO nanocomposites for microwave assisted adsorbate regenerationPresented at: International Conference on Metal-Organic Frameworks and Open Framework Compounds 2022Dresden, Germany, September 4, 2022.

Zhou, T., Wang, Z., & Sundmacher, K.A New Machine Learning Framework for Efficient MOF Discovery: Application to Hydrogen StoragePresented at: 14th International Symposium on Process Systems Engineering: PSE 2021+Kyoto, Japan, June 19, 2022.