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<p>This study systematically investigates the structure–activity relationships of 30 Ti-phenoxy-imine (FI-Ti) catalysts using machine learning (ML) approaches. Among the tested algorithms, XGBoost demonstrated superior predictive performance, achieving R<sup>2</sup> values of 0.998 (training set) and 0.859 (test set), with a cross-validated Q<sup>2</sup> of 0.617. Feature importance analysis identified three composite descriptors—ODI_HOMO_1_Neg_Average GGI2, ALIEmax GATS8d, and Mol_Size_L—as critical contributors, collectively accounting for &gt; 63% of the model’s predictive power. Polynomial feature expansion effectively captured nonlinear interactions between descriptors, while SHAP and ICE analyses enhanced interpretability, revealing threshold effects and descriptor-specific trends. However, the model’s generalizability may be constrained by the limited dataset size (30 samples) and reliance on density functional theory (DFT)-derived descriptors, necessitating experimental validation. Additionally, the study focused solely on ethylene polymerization at 40&#xa0;°C; broader applicability to diverse catalytic systems or reaction conditions requires further validation. These findings provide a data-driven framework for catalyst design, though future work should integrate experimental validation and expand datasets to refine predictive robustness.</p> Graphical abstract <p></p>

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Machine learning-based activity prediction of phenoxy-imine catalysts and its structure–activity relationship study

  • 期刊论文

  • 发表日期：

    2025年3月7日

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  • Xiaoke Zhou,
  • Sisi He,
  • Min Xiao,
  • Jing He,
  • Yuan Wang,
  • Yuanqin Zhu,
  • Haixiang He

摘要

This study systematically investigates the structure–activity relationships of 30 Ti-phenoxy-imine (FI-Ti) catalysts using machine learning (ML) approaches. Among the tested algorithms, XGBoost demonstrated superior predictive performance, achieving R2 values of 0.998 (training set) and 0.859 (test set), with a cross-validated Q2 of 0.617. Feature importance analysis identified three composite descriptors—ODI_HOMO_1_Neg_Average GGI2, ALIEmax GATS8d, and Mol_Size_L—as critical contributors, collectively accounting for > 63% of the model’s predictive power. Polynomial feature expansion effectively captured nonlinear interactions between descriptors, while SHAP and ICE analyses enhanced interpretability, revealing threshold effects and descriptor-specific trends. However, the model’s generalizability may be constrained by the limited dataset size (30 samples) and reliance on density functional theory (DFT)-derived descriptors, necessitating experimental validation. Additionally, the study focused solely on ethylene polymerization at 40 °C; broader applicability to diverse catalytic systems or reaction conditions requires further validation. These findings provide a data-driven framework for catalyst design, though future work should integrate experimental validation and expand datasets to refine predictive robustness.

Graphical abstract