Accelerated dinuclear palladium catalyst identification through unsupervised machine learning

Although machine learning bears enormous potential to accelerate developments in homogeneous catalysis, the frequent need for extensive experimental data can be a bottleneck for implementation. Here, we report an unsupervised machine learning workflow that uses only five experimental data points. It...

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Bibliographic Details
Published in:Science (American Association for the Advancement of Science) 2021-11, Vol.374 (6571), p.1134-1140
Main Authors: Hueffel, Julian A, Sperger, Theresa, Funes-Ardoiz, Ignacio, Ward, Jas S, Rissanen, Kari, Schoenebeck, Franziska
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Catalysis
Catalysts
Clustering
Data acquisition
Data points
Dimers
Experimental data
Learning algorithms
Ligands
Machine learning
Optimization
Palladium
Phosphine
Phosphines
Speciation
Synthesis
Unsupervised learning
Workflow
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Summary:Although machine learning bears enormous potential to accelerate developments in homogeneous catalysis, the frequent need for extensive experimental data can be a bottleneck for implementation. Here, we report an unsupervised machine learning workflow that uses only five experimental data points. It makes use of generalized parameter databases that are complemented with problem-specific in silico data acquisition and clustering. We showcase the power of this strategy for the challenging problem of speciation of palladium (Pd) catalysts, for which a mechanistic rationale is currently lacking. From a total space of 348 ligands, the algorithm predicted, and we experimentally verified, a number of phosphine ligands (including previously never synthesized ones) that give dinuclear Pd complexes over the more common Pd and Pd species.
ISSN:0036-8075
1095-9203
DOI:10.1126/science.abj0999