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Adaptive Optimization of Chemical Reactions with Minimal Experimental Information

Publication ,  Journal Article
Reker, D; Hoyt, EA; Bernardes, GJL; Rodrigues, T
Published in: Cell Reports Physical Science
November 18, 2020

Optimizing reaction conditions depends on expert chemistry knowledge and laborious exploration of reaction parameters. To automate this task and augment chemical intuition, we here report a computational tool to navigate search spaces. Our approach (LabMate.ML) integrates random sampling of 0.03%–0.04% of all search space as input data with an interpretable, adaptive machine-learning algorithm. LabMate.ML can optimize many real-valued and categorical reaction parameters simultaneously, with minimal computational resources and time. In nine prospective proof-of-concept studies pursuing distinctive objectives, we demonstrate how LabMate.ML can identify optimal goal-oriented conditions for several different chemistries and substrates. Double-blind competitions and the conducted expert surveys reveal that its performance is competitive with that of human experts. LabMate.ML does not require specialized hardware, affords quantitative and interpretable reactivity insights, and autonomously formalizes chemical intuition, thereby providing an innovative framework for informed, automated experiment selection toward the democratization of synthetic chemistry.

Duke Scholars

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Published In

Cell Reports Physical Science

DOI

EISSN

2666-3864

Publication Date

November 18, 2020

Volume

1

Issue

11

Related Subject Headings

  • 4016 Materials engineering
  • 4009 Electronics, sensors and digital hardware
  • 3403 Macromolecular and materials chemistry
 

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Reker, D., Hoyt, E. A., Bernardes, G. J. L., & Rodrigues, T. (2020). Adaptive Optimization of Chemical Reactions with Minimal Experimental Information. Cell Reports Physical Science, 1(11). https://doi.org/10.1016/j.xcrp.2020.100247
Reker, D., E. A. Hoyt, G. J. L. Bernardes, and T. Rodrigues. “Adaptive Optimization of Chemical Reactions with Minimal Experimental Information.” Cell Reports Physical Science 1, no. 11 (November 18, 2020). https://doi.org/10.1016/j.xcrp.2020.100247.
Reker D, Hoyt EA, Bernardes GJL, Rodrigues T. Adaptive Optimization of Chemical Reactions with Minimal Experimental Information. Cell Reports Physical Science. 2020 Nov 18;1(11).
Reker, D., et al. “Adaptive Optimization of Chemical Reactions with Minimal Experimental Information.” Cell Reports Physical Science, vol. 1, no. 11, Nov. 2020. Scopus, doi:10.1016/j.xcrp.2020.100247.
Reker D, Hoyt EA, Bernardes GJL, Rodrigues T. Adaptive Optimization of Chemical Reactions with Minimal Experimental Information. Cell Reports Physical Science. 2020 Nov 18;1(11).

Published In

Cell Reports Physical Science

DOI

EISSN

2666-3864

Publication Date

November 18, 2020

Volume

1

Issue

11

Related Subject Headings

  • 4016 Materials engineering
  • 4009 Electronics, sensors and digital hardware
  • 3403 Macromolecular and materials chemistry