The need to understand chemistry through data-driven analysis from a comprehensive collection of reaction data has long been recognized, and the development of chemical reaction databases has been conducted for decades, including commercial databases such as Reaxys, Scifinder, or Web of Science and open-source database like the Open Reaction Database. As a result, exploiting information in this large-scale database could accelerate the process of addressing challenging chemistry problems. Recent developments in big data and machine learning have revolutionized approaches for analyzing chemical data, and successful applications of these techniques have significantly advanced the research in chemical reactions, including reaction classification, product prediction, yield estimation, and s...[ Read more ]

The need to understand chemistry through data-driven analysis from a comprehensive collection of reaction data has long been recognized, and the development of chemical reaction databases has been conducted for decades, including commercial databases such as Reaxys, Scifinder, or Web of Science and open-source database like the Open Reaction Database. As a result, exploiting information in this large-scale database could accelerate the process of addressing challenging chemistry problems. Recent developments in big data and machine learning have revolutionized approaches for analyzing chemical data, and successful applications of these techniques have significantly advanced the research in chemical reactions, including reaction classification, product prediction, yield estimation, and synthesis planning.

In recent years, homogeneous nickel catalysis has been extensively studied in academia and industry. Remarkable advances have been made for many valuable reactions by using nickel as an effective catalyst, owing to its unique reactivity and cost efficiency. It would be beneficial to prepare a dataset to analyze this field from a fundamental point of view and provide more insight into the nickel-catalyzed reactions. We collected and analyzed a comprehensive dataset containing important papers in the field of C-O bond activation cross-coupling reactions and developed data mining tools to accelerate similar studies in the future.