Adaptive Optimization of Chemical Reactions with Minimal Experimental Information

Journal Article (Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • Reker, D; Hoyt, EA; Bernardes, GJL; Rodrigues, T

Published Date

  • November 18, 2020

Published In

Volume / Issue

  • 1 / 11

Electronic International Standard Serial Number (EISSN)

  • 2666-3864

Digital Object Identifier (DOI)

  • 10.1016/j.xcrp.2020.100247

Citation Source

  • Scopus