Publications of Zihao Wang

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.: A Novel Machine Learning-Based Optimization Approach for the Molecular Design of Solvents. ESCAPE-32, Toulouse, France (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)