Relative Generality and Risk: Quantitative Measures For Broad Catalyst Success

06 May 2024, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

The performance of chiral catalysts is typically evaluated against empirical reaction outputs like yield and selectivity with traditional analyses limited to a single model system. Expansion of the reaction space permits catalysts to be as-sessed for generality and this provides another useful metric for measuring the effectiveness of a catalyst. The catalyst generality algorithm will assign quantitative generality values to catalyst structures but such broad assessments are applied with the assumption that the reactions under evaluation are more or less the same by disregarding any inherent challenges associated with a particular reaction class. To address this limitation, we introduce two new met-rics, relative generality and risk. These are designed to correct for variations in reaction difficulty and enable a more nuanced evaluation of catalyst performance relative to the specific demands of each reaction. We show in a number of challenging examples that these metrics allow researchers to distinguish between catalysts genuinely exhibiting superior performance and those appearing favorable due to application toward less demanding reactions. This represents a significant advancement in quantifying catalyst success, with demonstrated applications in retrospective analyses and early insights into emerging catalyst classes.

Keywords

machine learning
asymmetric catalysis
Brønsted acids

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