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Creators/Authors contains: "Doyle, Abigail"

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  1. ; ; ; ; ; ; ; (, Tetrahedron Letters)
    Free, publicly-accessible full text available April 1, 2026
  2. ; ; ; ; (, Angewandte Chemie International Edition)
    Abstract The term “generality” has recently been popularized in synthetic chemistry, owing largely to the increasing use of high‐throughput technology for producing vast quantities of data and the emergence of data science tools to plan and interpret these experiments. Despite this, the term has not been clearly defined, and there is no standardized approach toward developing a method with a diverse (general) scope. This minireview will examine different emerging strategies toward achieving generality using selected examples and aims to give the reader an overview of modern workflows that have been used to expedite this pursuit. 
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    Free, publicly-accessible full text available October 6, 2026
  3. ; ; ; ; ; ; ; ; (, ACS Catalysis)
    Free, publicly-accessible full text available February 7, 2026
  4. ; ; (, Journal of the American Chemical Society)
    Full Text Available 
  5. ; ; ; ; ; ; ; ; ; (, ACS Catalysis)
    Full Text Available 
  6. ; ; ; (, Journal of the American Chemical Society)
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  7. ; ; ; (, Journal of the American Chemical Society)
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  8. ; ; (, Journal of the American Chemical Society)
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  9. ; ; ; (, European Journal of Organic Chemistry)
    Abstract A key challenge in synthetic chemistry is the selection of high‐performing ligands for cross‐coupling reactions. To address this challenge, this work presents a classification workflow to identify physicochemical descriptors that bin monophosphine ligands as active or inactive in Ni‐catalyzed Suzuki‐Miyaura coupling reactions. Using five previously published high‐throughput experimentation datasets for training, we found that a binary classifier using a phosphine's minimum buried volume and Boltzmann‐averaged minimum electrostatic potential is most effective at distinguishing high and low‐yielding ligands. Experimental validations are also presented. Using the two physicochemical descriptors from the binary classifier to represent the chemical space of monophosphine ligands leads to a more predictive guide for structure‐reactivity relationships compared with classic chemical space representations. 
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  10. ; ; ; (, Journal of the American Chemical Society)
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