Preprint
Aug 2026
Discovering Physically Interpretable Mathematical Expression for Predicting CO2 Adsorption in Metal-Organic Frameworks via Machine Learning-Symbolic Regression
This work presents a machine learning-symbolic regression strategy to develop a physically interpretable formula for predicting low pressure CO2 adsorption capacity in hypothetical metal-organic frameworks (hMOFs), and proposes a physics-guided expression that enables efficient prediction and provides clearer insight i...
Yimin Shao, Sheng-Ling Ma, Sheng-Hong Ju et al.
· 0 citations