2 papers
cond-mat.mtrl-sci2026
Discovering Physically Interpretable Mathematical Expression for Predicting CO2 Adsorption in Metal-Organic Frameworks via Machine Learning-Symbolic Regression
Yimin Shao, Shengluo Ma, Shenghong Ju +2
This work presents a machine learning-symbolic regression (ML-SR) strategy to develop a physically interpretable formula for predicting low pressure CO2 adsorption capacity in hypo…
cond-mat.mtrl-sci2024
High-throughput discovery of metal oxides with high thermoelectric performance via interpretable feature engineering on small data
Shengluo Ma, Yongchao Rao, Xiang Huang +1
In this work, we have proposed a data-driven screening framework combining the interpretable machine learning with high-throughput calculations to identify a series of metal oxides…