Issue 12, 2024
From the journal:

New Journal of Chemistry

Navigating epoxidation complexity: building a data science toolbox to design vanadium catalysts†

José Ferraz-Caetano, ORCID logo *a   Filipe Teixeira ORCID logo b  and  M. Natália D. S. Cordeiro ORCID logo *a  

* Corresponding authors

a LAQV-REQUIMTE – Department of Chemistry and Biochemistry – Faculty of Sciences, University of Porto – Rua do Campo Alegre, s/n, Porto 4169-007, Portugal
E-mail: jose.caetano@fc.up.pt, ncordeir@fc.up.pt

b CQUM – Centre of Chemistry, University of Minho, Campus de Gualtar, Braga 4710-057, Portugal

Abstract

This communication presents a novel approach to set up a machine learning-ready database for epoxidation reactions, focusing on vanadium catalysts. Utilising data driven analysis, we identified key reaction yield trends through chemical descriptors, providing insights for catalyst design and reaction optimisation.

Graphical abstract: Navigating epoxidation complexity: building a data science toolbox to design vanadium catalysts

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Article information

DOI
https://doi.org/10.1039/D3NJ05784D
Article type
Communication
Submitted
16 Dec 2023
Accepted
24 Feb 2024
First published
26 Feb 2024
This article is Open Access
Creative Commons BY license

New J. Chem., 2024,48, 5097-5100

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Navigating epoxidation complexity: building a data science toolbox to design vanadium catalysts

J. Ferraz-Caetano, F. Teixeira and M. N. D. S. Cordeiro, New J. Chem., 2024, 48, 5097 DOI: 10.1039/D3NJ05784D

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