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Development of a Computer-Guided Workflow for Catalyst Optimization. Descriptor Validation, Subset Selection, and

Jeremy J Henle¹, Andrew F Zahrt¹, Brennan T Rose¹

¹Roger Adams Laboratory, Department of Chemistry, University of Illinois, Urbana, Illinois 61801, United States.

Journal of the American Chemical Society

|June 23, 2020

Summary

This summary is machine-generated.

This study introduces a statistical approach to streamline enantioselective catalyst development, moving beyond traditional empiricism. This informatics workflow enhances catalyst optimization and predictive modeling accuracy.

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Area of Science:

Catalysis
Computational Chemistry
Chemical Informatics

Background:

Enantioselective catalyst development traditionally relies on empirical methods, which are limited by human expertise.
A need exists for more systematic and data-driven approaches to catalyst optimization.

Purpose of the Study:

To present a complementary approach to catalyst optimization using statistical methods.
To develop and validate an informatics workflow for streamlining catalyst development.

Main Methods:

Utilized statistical methods for catalyst optimization.
Validated conformation-dependent molecular descriptors in case studies.
Investigated data requirements for predictive models using various methods.
Compared models from algorithmically selected and commercially available training sets.
Employed unsupervised learning to augment limited data sets.

Main Results:

Conformation-dependent molecular representations were validated as critical.
Determined the data volume necessary for accurate predictive models.
Demonstrated improved model accuracy through data augmentation techniques.

Conclusions:

Statistical methods offer a powerful, complementary approach to empirical catalyst development.
The developed informatics workflow enhances efficiency and accuracy in catalyst optimization.
Data augmentation and careful descriptor selection are key to robust predictive models.