Hueffel Julian A, Sperger Theresa, Funes-Ardoiz Ignacio, Ward Jas S, Rissanen Kari, Schoenebeck Franziska
Institute of Organic Chemistry, RWTH Aachen University; Landoltweg 1, 52074 Aachen, Germany.
Department of Chemistry, University of Jyväskylä; P.O. Box 35, 40014 Jyväskylä, Finland.
Science. 2021 Nov 26;374(6571):1134-1140. doi: 10.1126/science.abj0999. Epub 2021 Nov 25.
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.
尽管机器学习在加速均相催化发展方面具有巨大潜力，但频繁需要大量实验数据可能成为实施的瓶颈。在此，我们报告一种无监督机器学习工作流程，该流程仅使用五个实验数据点。它利用广义参数数据库，并辅以特定问题的计算机数据采集和聚类。我们展示了这种策略对于钯（Pd）催化剂形态这一具有挑战性问题的强大作用，目前该问题缺乏机理依据。在总共348种配体的空间中，该算法预测并经我们实验验证了一些膦配体（包括以前从未合成过的），这些配体生成双核钯配合物的比例高于更常见的钯和钯物种。
