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


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



Machine learning · enantioselective catalysis · chemoinformatics · catalyst optimization


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.




How to Cite

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