Publications of Zihao Wang

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

Wang, Z.; Zhou, T.; Sundmacher, K.: Data‐driven integrated design of solvents and extractive distillation processes. AIChE Journal 69 (12), e18236 (2023)

Zhou, T.; Gui, C.; Sun, L.; Hu, Y.; Lyu, H.; Wang, Z.; Song, Z.; Yu, G.: Energy Applications of Ionic Liquids: Recent Developments and Future Prospects. Chemical Reviews 123, pp. 12170 - 12253 (2023)

Qin, H.; Wang, Z.; Song, Z.; Zhang, X.; Zhou, T.: High-Throughput Computational Screening of Ionic Liquids for Butadiene and Butene Separation. Processes 10 (1), 165 (2022)

Wang, Z.; Zhou, T.; Sundmacher, K.: Interpretable machine learning for accelerating the discovery of metal-organic frameworks for ethane/ethylene separation. Chemical Engineering Journal 444, 136651 (2022)

Wang, Z.; Zhou, Y.; Zhou, T.; Sundmacher, K.: Identification of Optimal Metal-Organic Frameworks by Machine Learning: Structure Decomposition, Feature Integration, and Predictive Modeling. Computers & Chemical Engineering 160, 107739 (2022)

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

Zhang, X.; Sethi, S.; Wang, Z.; Zhou, T.; Qi, Z.; Sundmacher, K.: A neural recommender system for efficient adsorbent screening. Chemical Engineering Science 259, 117801 (2022)

Qin, H.; Wang, Z.; Zhou, T.; Song, Z.: Comprehensive Evaluation of COSMO-RS for Predicting Ternary and Binary Ionic Liquid-Containing Vapor–Liquid Equilibria. Industrial & Engineering Chemistry Research 60 (48), pp. 17761 - 17777 (2021)

Wang, Z.; Song, Z.; Zhou, T.: Machine Learning for Ionic Liquid Toxicity Prediction. Processes 9 (1), 65 (2021)

Wang, Z.; Zhou, T.; Sundmacher, K.: Molecular Property Targeting for Optimal Solvent Design in Extractive Distillation Processes. In: 33rd European Symposium on Computer Aided Process Engineering: Computer Aided Chemical Engineering, pp. 1247 - 1252 (Eds. Kokossis, A.; Georgiadis, M. C.; Pistikopoulos, E.). 33rd European Symposium on Computer Aided Process Engineering, Athen, Greece, June 18, 2023 - June 21, 2023. Elsevier, Amsterdam/Netherlands (2023)

Wang, Z.; Zhou, T.; Sundmacher, K.: A Novel Machine Learning-Based Optimization Approach for the Molecular Design of Solvents. In: 32nd European Symposium on Computer Aided Process Engineering, pp. 1477 - 1482. 32nd European Symposium on Computer Aided Process Engineering : ESCAPE 32, Toulouse, France, June 12, 2022 - June 15, 2022. Elsevier (2022)

Zhou, T.; Wang, Z.; Sundmacher, K.: A New Machine Learning Framework for Efficient MOF Discovery: Application to Hydrogen Storage. In: 14th International Symposium on Process Systems Engineering:, pp. 1807 - 1812 (Eds. Yamashita, Y.; Kano, M.). 14th International Symposium on Process Systems Engineering - PSE 2021+, Kyoto, Japan, June 19, 2022 - June 23, 2022. Elsevier (2022)

Wang, Z.; Zhou, T.; Sundmacher, K.: Data-driven integrated design of solvents and extractive distillation processes. 2023 AIChE Annual Meeting, Orlando, USA (2023)

Wang, Z.; Zhou, T.; Sundmacher, K.: A Novel Machine Learning-Based Optimization Approach for the Molecular Design of Solvents. ESCAPE-32, Toulouse, France (2022)

Wang, Z.; Zhou, T.; Sundmacher, K.: Molecular Property Targeting for Optimal Solvent Design in Extractive Distillation Processes. ESCAPE-33, Athens, Greece (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 regeneration. International Conference on Metal-Organic Frameworks and Open Framework Compounds 2022, Dresden, Germany (2022)

Zhou, T.; Wang, Z.; Sundmacher, K.: A New Machine Learning Framework for Efficient MOF Discovery: Application to Hydrogen Storage. 14th International Symposium on Process Systems Engineering: PSE 2021+, Kyoto, Japan (2022)