CatalySeed: A reaction database for ruthenium-catalyzed ethenolysis of seed oils with applications in machine learning
Ethenolysis of unsaturated seed oils is an atom-efficient metathesis reaction that enables α-olefin production and fine chemical synthesis. By upcycling complex biobased molecules into value-added products, it supports circular chemical processes. In this study, we present a curated data set to supp...
| Autores: | , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2026 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:dnet:digitalcsic_::28c2bc0f62a96cc6443023e76148a45c |
| Acceso en línea: | http://hdl.handle.net/10261/427116 |
| Access Level: | acceso abierto |
| Palabra clave: | Ethenolysis Cheminformatics Oleate Machine learning Ruthenium |
| Sumario: | Ethenolysis of unsaturated seed oils is an atom-efficient metathesis reaction that enables α-olefin production and fine chemical synthesis. By upcycling complex biobased molecules into value-added products, it supports circular chemical processes. In this study, we present a curated data set to support machine learning (ML) analysis of catalytic performance in the ethenolysis of seed oils. Through a detailed classification of 768 entries and 217 catalysts, along with the integration of the ROBERT ML framework, with the CatalySeed database we identify key electronic descriptors that correlate with experimental outcomes. Binary classification models for TON (threshold ≥ 0.75 × 106) and % selectivity (≥90%) achieved strong performance, suggesting that higher Ru partial charge tends to correlate with higher TON, while lower metal d-orbital character is generally associated with higher selectivity. These findings illustrate how this database, available through an open-access web server, enables ML to uncover predictive trends, supporting catalyst design strategies beyond conventional computational approaches for the transformation of renewable feedstocks. |
|---|