cff-version: 1.2.0
abstract: "<p>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.</p>"
authors:
  - family-names: Kalikadien
    given-names: Adarsh V.
    orcid: "https://orcid.org/0000-0002-5414-3424"
  - family-names: Pedrazzi
    given-names: Francesco
    orcid: "https://orcid.org/0009-0007-2853-2657"
  - family-names: Valsecchi
    given-names: Cecile
    orcid: "https://orcid.org/0000-0002-5535-053X"
  - family-names: Lefort
    given-names: Laurent
    orcid: "https://orcid.org/0000-0003-2973-6540"
  - family-names: Pidko
    given-names: Evgeny
    orcid: "https://orcid.org/0000-0001-9242-9901"
title: "Data underlying the chapter: Machine-Learning-Guided Optimization of Phosphine-based Ligands for Nickel-Catalyzed Addition of Arylboronic Acids to Nitriles"
keywords:
version: 1
identifiers:
  - type: doi
    value: 10.4121/e77cddf1-7ffc-4cbb-a3c9-bf8adc352192.v1
license: CC0
date-released: 2025-09-26