Publications of Zihao Wang

Journal Article (7)

2022
Journal Article
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)
Journal Article
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)
Journal Article
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)
Journal Article
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)
Journal Article
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)
2021
Journal Article
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)
Journal Article
Wang, Z.; Song, Z.; Zhou, T.: Machine Learning for Ionic Liquid Toxicity Prediction. Processes 9 (1), 65 (2021)

Conference Paper (2)

2022
Conference Paper
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: ESCAPE 32, Vol. 51, pp. 1477 - 1482. 32nd European Symposium on Computer Aided Process Engineering : ESCAPE 32, Toulouse, France, June 12, 2022 - June 15, 2022. Elsevier (2022)
Conference Paper
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)

Talk (1)

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

Poster (1)

2022
Poster
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)
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