Relative Generality and Risk: Quantitative Measures for Broad Catalyst Success

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 assessed 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 metrics: relative generality and risk. These are designed to correct for variations in reaction difficulty and enable a more nuanced evaluation of the 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.

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Publication Details

Journal
ACS Catalysis
Published
2024-10-31
DOI
https://doi.org/10.1021/acscatal.4c04395
Citations
10
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
1.17

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article

Relative Generality and Risk: Quantitative Measures for Broad Catalyst Success

Jolene P. Reid, H Russell, Jasemine Handjaya, Michal Sanocki
10 citations
ACS Catalysis
Machine Learning in Materials Science
1.17
article

Relative Generality and Risk: Quantitative Measures for Broad Catalyst Success

Jolene P. Reid, H Russell, Jasemine Handjaya, Michal Sanocki
article en
10 citations

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 assessed 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 metrics: relative generality and risk. These are designed to correct for variations in reaction difficulty and enable a more nuanced evaluation of the 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.

ACS CatalysisVol. 14(22)
University of British Columbia (CA)
Canada Foundation for Innovation, Natural Sciences and Engineering Research Council of Canada, Engineering and Physical Sciences Research Council
Openalex Percentile: Top 18%
Machine Learning in Materials Science
1.17
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Relative Generality and Risk: Quantitative Measures for Broad Catalyst Success — Jolene P. Reid, H Russell, et al. · ACS Catalysis (2024) | TGRS Research Map | TGRS