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
-
Affiliation as printed
Institute of Organic Chemistry, RWTH Aachen University; Landoltweg 1, 52074 Aachen, Germany
Landoltweg 1, 52074 Aachen, Germany
-
Affiliation as printed
Institute of Organic Chemistry, RWTH Aachen University; Landoltweg 1, 52074 Aachen, Germany
Landoltweg 1, 52074 Aachen, Germany
-
Affiliation as printed
Institute of Organic Chemistry, RWTH Aachen University; Landoltweg 1, 52074 Aachen, Germany
Landoltweg 1, 52074 Aachen, Germany
-
Affiliation as printed
Department of Chemistry, University of Jyväskylä; P.O. Box 35, 40014 Jyväskylä, Finland
-
Affiliation as printed
Department of Chemistry, University of Jyväskylä; P.O. Box 35, 40014 Jyväskylä, Finland
-
Affiliation as printed
Institute of Organic Chemistry, RWTH Aachen University; Landoltweg 1, 52074 Aachen, Germany
Landoltweg 1, 52074 Aachen, Germany
Cited by 176 stored of 176
No patents citing this paper on Lens.org (checked 2026-10-06).