The Leveraging Machine Learning for Enantioselective Catalysis: From Dream to Reality

Authors

  • N. Ian Rinehart
  • Andrew F. Zahrt
  • Scott Denmark University of Illinois at Urbana-Champaign

DOI:

https://doi.org/10.2533/chimia.2021.592

Keywords:

Machine learning · enantioselective catalysis · chemoinformatics · catalyst optimization

Abstract

Catalyst optimization for enantioselective transformations has traditionally relied on empirical evaluation of catalyst properties. Although this approach has been successful in the past is it intrinsically limtied and inefficient.  To address this problem, our laboratory has developed a fully informatics guided workflow to leverage the power of artificial intelligence (AI) and machine learning (ML) to accelerate the discovery and optimization any class of catalyst for any transformation. This approach is mechanistically agnostic, but also serves as a discovery platform to identify high performing catalysts that can be subsequently investigated by with physical organic methods to identify the origins of selectivity.

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Published

2021-08-17

How to Cite

[1]
N. I. Rinehart, A. F. Zahrt, S. Denmark, Chimia 2021, 75, 592, DOI: 10.2533/chimia.2021.592.