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Accelerated dinuclear palladium catalyst identification through unsupervised machine learning

Science, vol. 374, pp. 1134–1140

Abstract

Learning to stabilize palladium dimers Catalyst optimization is often difficult to do rationally. Once something works, it may be unclear which specific features underpin the performance. A case in point is the stabilization of palladium(I) dimers, which has relied on a very small class of phosphine ligands. Hueffel et al . used machine learning to search for patterns in this known class of ligands and thereby guide the discovery of variants that likewise stabilize the dimers. The authors were able to synthesize eight previously unreported dimers. —JSY

Authors 6

  1. RWTH Aachen University

    Affiliation as printed

    Institute of Organic Chemistry, RWTH Aachen University; Landoltweg 1, 52074 Aachen, Germany

    Landoltweg 1, 52074 Aachen, Germany

  2. RWTH Aachen University

    Affiliation as printed

    Institute of Organic Chemistry, RWTH Aachen University; Landoltweg 1, 52074 Aachen, Germany

    Landoltweg 1, 52074 Aachen, Germany

  3. RWTH Aachen University

    Affiliation as printed

    Institute of Organic Chemistry, RWTH Aachen University; Landoltweg 1, 52074 Aachen, Germany

    Landoltweg 1, 52074 Aachen, Germany

  4. University of Jyväskylä

    Affiliation as printed

    Department of Chemistry, University of Jyväskylä; P.O. Box 35, 40014 Jyväskylä, Finland

  5. University of Jyväskylä

    Affiliation as printed

    Department of Chemistry, University of Jyväskylä; P.O. Box 35, 40014 Jyväskylä, Finland

  6. RWTH Aachen University

    Affiliation as printed

    Institute of Organic Chemistry, RWTH Aachen University; Landoltweg 1, 52074 Aachen, Germany

    Landoltweg 1, 52074 Aachen, Germany

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References 67