Journal
JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
Volume 142, Issue 26, Pages 11578-11592Publisher
AMER CHEMICAL SOC
DOI: 10.1021/jacs.0c04715
Keywords
-
Categories
Funding
- W. M. Keck Foundation
- National Science Foundation [NSF CHE1900617]
- University of Illinois
- National Science Foundation
- Janssen Research Development LLC, San Diego
Ask authors/readers for more resources
Modern, enantioselective catalyst development is driven largely by empiricism. Although this approach has fostered the introduction of most of the existing synthetic methods, it is inherently limited by the skill, creativity, and chemical intuition of the practitioner. Herein, we present a complementary approach to catalyst optimization in which statistical methods are used at each stage to streamline development. To construct the optimization informatics workflow, a number of critical components had to be subjected to rigorous validation. First, the critically important molecular descriptors were validated in two case studies to establish the importance of conformation-dependent molecular representations. Next, with a large data set available, it was possible to investigate the amount of data necessary to make predictive models with different modeling methods. Given the commercial availability of many catalyst structures, it was possible to compare models generated with algorithmically selected training sets and commercially available training sets. Finally, the augmentation of limited data sets is demonstrated in a method informed by unsupervised learning to restore the accuracy of the generated models.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available