ArticleChemical science2020
Quantum-mechanical transition-state model combined with machine learning provides catalyst design features for selective Cr olefin oligomerization.
Article in Chemical science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Staged Diversity-Constrained Machine Learning for High-Dimensional Reaction Condition Optimization.Angewandte Chemie (International ed. in English) · 2026Article
- Decoding polyethylene formation in Cr/PNP catalyzed ethylene oligomerizationChemical science · 2026Article
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- AI Approaches to Homogeneous Catalysis with Transition Metal Complexes.ACS catalysis · 2025Review
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- Data-driven discovery of active phosphine ligand space for cross-coupling reactions.Chemical science · 2024Article
- On Accelerating Substrate Optimization Using Computational Gibbs Energy Barriers: A Numerical Consideration Utilizing a Computational Data Set.ACS omega · 2024Article
- Reaction performance prediction with an extrapolative and interpretable graph model based on chemical knowledge.Nature communications · 2023Article
- A compact review of progress and prospects of deep learning in drug discovery.Journal of molecular modeling · 2023Review
- Graph-Driven Reaction Discovery: Progress, Challenges, and Future Opportunities.The journal of physical chemistry. A · 2022Review
- Article
- Machine learning and semi-empirical calculations: a synergistic approach to rapid, accurate, and mechanism-based reaction barrier prediction.Chemical science · 2022Article
- Reaction-based machine learning representations for predicting the enantioselectivity of organocatalysts.Chemical science · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The use of data science tools to provide the emergence of non-trivial chemical features for catalyst design is an important goal in catalysis science. Additionally, there is currently no general strategy for computational homogeneous, molecular catalyst design. Here, we report the unique combination of an experimentally verified DFT-transition-state model with a random forest machine learning model in a campaign to design new molecular Cr phosphine imine (Cr(P,N)) catalysts for selective ethylene oligomerization, specifically to increase 1-octene selectivity. This involved the calculation of 1-hexene : 1-octene transition-state selectivity for 105 (P,N) ligands and the harvesting of 14 descriptors, which were then used to build a random forest regression model. This model showed the emergence of several key design features, such as Cr-N distance, Cr-α distance, and Cr distance out of pocket, which were then used to rapidly design a new generation of Cr(P,N) catalyst ligands that are predicted to give >95% selectivity for 1-octene.
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Registered trials
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