A Comprehensive Discovery Platform for Organophosphorus Ligands for Catalysis

Tobias Gensch*, Gabriel Dos Passos Gomes, Pascal Friederich, Ellyn Peters, Théophile Gaudin, Robert Pollice, Kjell Jorner, Akshatkumar Nigam, Michael Lindner-D'Addario, Matthew S. Sigman, Alán Aspuru-Guzik

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

102 Citations (Scopus)

Abstract

The design of molecular catalysts typically involves reconciling multiple conflicting property requirements, largely relying on human intuition and local structural searches. However, the vast number of potential catalysts requires pruning of the candidate space by efficient property prediction with quantitative structure-property relationships. Data-driven workflows embedded in a library of potential catalysts can be used to build predictive models for catalyst performance and serve as a blueprint for novel catalyst designs. Herein we introduce kraken, a discovery platform covering monodentate organophosphorus(III) ligands providing comprehensive physicochemical descriptors based on representative conformer ensembles. Using quantum-mechanical methods, we calculated descriptors for 1558 ligands, including commercially available examples, and trained machine learning models to predict properties of over 300000 new ligands. We demonstrate the application of kraken to systematically explore the property space of organophosphorus ligands and how existing data sets in catalysis can be used to accelerate ligand selection during reaction optimization.

Original languageEnglish
Pages (from-to)1205-1217
Number of pages13
JournalJournal of the American Chemical Society
Volume144
Issue number3
DOIs
Publication statusPublished - 26-Jan-2022
Externally publishedYes

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