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

ABSTRACT: We present a robotic chemical discovery system capable of navigating a chemical space based on a learned general association between molecular structures and reactivity, while incorporating a neural network model that can process data from online analytics and assess reactivity without knowing the identity of the reagents. Working in conjunction with this learned knowledge, our robotic platform is able to autonomously explore a large number of potential reactions and assess the reactivity of mixtures, including unknown chemical spaces, regardless of the identity of the starting materials. Through the system, we identified a range of chemical reactions and products, some of which were well-known, some new but predictable from known pathways, and some unpredictable reactions that yielded new

SUBMITTER: Caramelli D 

PROVIDER: S-EPMC8620554 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature