| Autor: |
Sadiq Z; Key Laboratory of Engineering Plastics, Beijing National Laboratory for Molecular Science, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China.; University of Chinese Academy of Sciences, Beijing 100049, China., Yang W; PetroChina Petrochemical Research Institute, Beijing, 102206, China., Meraz MM; Key Laboratory of Engineering Plastics, Beijing National Laboratory for Molecular Science, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China.; University of Chinese Academy of Sciences, Beijing 100049, China., Yang W; PetroChina Petrochemical Research Institute, Beijing, 102206, China., Sun WH; Key Laboratory of Engineering Plastics, Beijing National Laboratory for Molecular Science, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China.; University of Chinese Academy of Sciences, Beijing 100049, China. |
| Abstrakt: |
In anticipation of the correlations between catalyst structures and their properties, the catalytic activities of 2-imino-1,10-phenanthrolyl iron and cobalt metal complexes are quantitatively investigated via linear machine learning (ML) algorithms. Comparatively, the Ridge Regression (RR) model has captured more robust predictive performance compared with other linear algorithms, with a correlation coefficient value of R 2 = 0.952 and a cross-validation value of Q 2 = 0.871. It shows that different algorithms select distinct types of descriptors, depending on the importance of descriptors. Through the interpretation of the RR model, the catalytic activity is potentially related to the steric effect of substituents and negative charged groups. This study refines descriptor selection for accurate modeling, providing insights into the variation principle of catalytic activity. |
