Combining machine learning with physical knowledge in thermodynamic modeling of fluid mixtures
Thermophysical properties of fluid mixtures are important in many fields of science and
engineering. However, experimental data are scarce in this field, so prediction methods are …
engineering. However, experimental data are scarce in this field, so prediction methods are …
Leveraging Machine Learning for Metal–Organic Frameworks: A Perspective
Metal–organic frameworks (MOFs) have attracted tremendous interest because of their
tunable structures, functionalities, and physiochemical properties. The nearly infinite …
tunable structures, functionalities, and physiochemical properties. The nearly infinite …
Gaussian process regression for prediction of hydrogen adsorption temperature–pressure dependence curves in metal–organic frameworks
Z Cao, X Wu, B Tang, W Cai - Chemical Engineering Journal, 2023 - Elsevier
Abstract A Gaussian Process Regression (GPR) model was proposed for high-throughput
prediction of H 2 adsorption isotherms of MOFs at varied temperatures based on classical …
prediction of H 2 adsorption isotherms of MOFs at varied temperatures based on classical …
Hydrocarbon Extraction with Ionic Liquids
Separation and reaction processes are key components employed in the modern chemical
industry, and the former accounts for the majority of the energy consumption therein. In …
industry, and the former accounts for the majority of the energy consumption therein. In …
Insights into deep learning framework for molecular property prediction based on different tokenization algorithms
J Yan, Z Zhang, M Meng, J Li, L Sun - Chemical Engineering Science, 2024 - Elsevier
With the rapid development of deep learning, research on quantitative structure–property
relationships based on deep learning has received widespread attention. The deep learning …
relationships based on deep learning has received widespread attention. The deep learning …