71 citations · 94 across the 4 of their papers we have counts for
4 papers
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…
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…
Exploring high thermal conductivity polymers via interpretable machine learning with physical descriptors
Xiang Huang, Shengluo Ma, C. Y. Zhao +2
The efficient and economical exploitation of polymers with high thermal conductivity is essential to solve the issue of heat dissipation in organic devices. Currently, the experime…
Enhancing thermoelectric properties of isotope graphene nanoribbons via machine learning guided manipulation of disordered antidots and interfaces
Xiang Huang, Shengluo Ma, Haidong Wang +4
Structural manipulation at the nanoscale breaks the intrinsic correlations among different energy carrier transport properties, achieving high thermoelectric performance. However,…