Data underlying the chapter: Machine-Learning-Guided Optimization of Phosphine-based Ligands for Nickel-Catalyzed Addition of Arylboronic Acids to Nitriles

DOI:10.4121/e77cddf1-7ffc-4cbb-a3c9-bf8adc352192.v1
The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/e77cddf1-7ffc-4cbb-a3c9-bf8adc352192

Datacite citation style

Kalikadien, Adarsh V.; Pedrazzi, Francesco; Valsecchi, Cecile; Lefort, Laurent; Pidko, Evgeny (2025): Data underlying the chapter: Machine-Learning-Guided Optimization of Phosphine-based Ligands for Nickel-Catalyzed Addition of Arylboronic Acids to Nitriles. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/e77cddf1-7ffc-4cbb-a3c9-bf8adc352192.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

We used high-throughput experimentation, density functional theory and machine learning to guide optimization of bisphosphine ligands for the nickel-catalyzed addition of arylboronic acids to nitriles. This dataset contains the version of the supporting information as published with this chapter, all code and data to reproduce the results and use the same approach on new datasets, an overview of the calculated descriptors, an overview of the ligands and the experimental results and finally an interactive version of the ensemble prediction made with the transfer learning approach presented in this paper.

History

  • 2025-09-26 first online, published, posted

Publisher

4TU.ResearchData

Format

DFT output: log files, CREST output: folder per ligand, code: .py, numerical data: .csv

Organizations

TU Delft, Faculty of Applied Sciences, Department of Chemical Engineering

DATA

Files (5)