Computer-assisted design of asymmetric PNP ligands for ethylene tri-/tetramerization: A combined DFT and artificial neural network approach

• Published in: Journal of catalysis Vol. 418; pp. 121 - 129
• Main Authors: Fan, Haonan, Yang, Xiaodie, Ma, Jing, Hao, Biaobiao, Alam, Fakhre, Huang, Xumeng, Wang, Aixi, Jiang, Tao
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.02.2023
• Subjects: Artificial neural network; DFT; Ethylene tetramerization; Ethylene trimerization; Ethylene trimerization; DFT; Artificial neural network; Ethylene tetramerization
• ISSN: 0021-9517; 1090-2694
• DOI: 10.1016/j.jcat.2023.01.011

[Display omitted] •A combined density functional theory (DFT) and artificial neural network (ANN) approach was developed for designing asymmetric PNP ligands.•The ANN-based models have satisfactory prediction accuracy.•Steric properties were found to dominate catalytic performance compared to electronic properties. The combination of computational and experimental sciences accelerates the design and development of molecular catalysts. A general strategy for developing ethylene oligomerization catalysts is still lacking. Consequently, herein, we proposed a widely applicable strategy for designing ethylene oligomerization catalysts. We combined density functional theory (DFT) and an artificial neural network (ANN) to establish a relation between catalyst structure and performance. The structure optimization and electronic calculation of a series of asymmetric PNP/Cr active species were conducted using DFT, and the steric and electronic descriptors were extracted to establish datasets. The catalyst prediction model was constructed using ANN and the leave-one-out cross-validation (LOOCV) method was used to verify the generalization ability of the models. The optimized ANN-based models used to predict 1-hexene and 1-octene selectivity exhibited high R2 values, which indicates satisfactory prediction accuracy of the models. We designed new PNP ligands and successfully predicted the ethylene oligomerization performance of PNP/Cr precatalysts using ANN-based models, which were verified through experiments. In addition, we found that the steric properties more significantly affect the performance of precatalysts than the electronic properties.