Deriving intuition in catalyst design with machine learning

Article Chemistry, Multidisciplinary

Discovering New Chemistry with an Autonomous Robotic Platform Driven by a Reactivity-Seeking Neural Network

Dario Caramelli et al.

Summary: The robotic chemical discovery system presented in the study is able to autonomously explore a large number of potential reactions and assess the reactivity of mixtures, including well-known, predictable, and unpredictable chemical reactions, without the need for human input. Through the validation of the system, new photochemical reactions and reactivity modes for well-known reagents were discovered, highlighting the possibility of a reactivity-first robotic discovery of unknown reaction methodologies.

ACS CENTRAL SCIENCE (2021)

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Article Chemistry, Multidisciplinary

Data-driven catalyst optimization for stereodivergent asymmetric synthesis by iridium/boron hybrid catalysis

Hongyu Chen et al.

Summary: This study presents the identification of asymmetric two-component iridium/boron hybrid catalyst systems for selective access to all the possible isomers of chiral carboxylic acids bearing contiguous stereocenters. This chemoselective and stereodivergent asymmetric catalysis is significant for late-stage structural modifications of drugs and their derivatives.

CELL REPORTS PHYSICAL SCIENCE (2021)

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Article Chemistry, Multidisciplinary