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Prediction of catalytic activities of bis(imino)pyridine metal complexes by machine learning
Author(s) -
Yang Wenhong,
Fidelis Timothy Tizhe,
Sun WenHua
Publication year - 2020
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.26160
Subject(s) - polyolefin , catalysis , artificial neural network , pyridine , polymerization , chemistry , decipher , computer science , artificial intelligence , organic chemistry , polymer , bioinformatics , layer (electronics) , biology
Abstract This work demonstrates the potential of machine learning (ML) method to predict catalytic activity of transition metal complex precatalyst toward ethylene polymerization. For this purpose, 294 complexes and 15 molecular descriptors were selected to build the artificial neural network (ANN) model. The catalytic activity can be well predicted by the obtained ANN model, which was further validated by external complexes. Boruta algorithm was employed to explicitly decipher the importance of descriptors, illustrating the conjugated bond structure, and bulky substitutions are favorable for catalytic activity. The present work indicates that ML could give useful guidance for the new design of homogenous polyolefin catalyst.