Towards Data‐Driven Design of Asymmetric Hydrogenation of Olefins: Database and Hierarchical Learning

• Published in: Angewandte Chemie International Edition Vol. 60; no. 42; pp. 22804 - 22811
• Main Authors: Xu, Li‐Cheng, Zhang, Shuo‐Qing, Li, Xin, Tang, Miao‐Jiong, Xie, Pei‐Pei, Hong, Xin
• Format: Journal Article
• Language: English
• Published: Weinheim Wiley Subscription Services, Inc 11.10.2021
• Edition: International ed. in English
• Subjects: Alkenes; asymmetric hydrogenation; Asymmetry; Catalysis; Catalysts; Chemical synthesis; data-driven design; Design; Enantiomers; enantioselectivity prediction; hierarchical learning; Hydrogenation; Learning algorithms; Machine learning; Optimization; Substrates

Asymmetric hydrogenation of olefins is one of the most powerful asymmetric transformations in molecular synthesis. Although several privileged catalyst scaffolds are available, the catalyst development for asymmetric hydrogenation is still a time‐ and resource‐consuming process due to the lack of pr...