Machine-Learning-Guided Discovery of Electrochemical Reactions

The molecular structures synthesizable by organic chemists
dictate
the molecular functions they can create. The invention and development
of chemical reactions are thus critical for chemists to access new
and desirable functional molecules in all disciplines of organic chemistry.
This work seeks to expedite the exploration of emerging areas of organic
chemistry by devising a machine-learning-guided workflow for reaction
discovery. Specifically, this study uses machine learning to predict
competent electrochemical reactions. To this end, we first develop
a molecular representation that enables the production of general
models with limited training data. Next, we employ automated experimentation
to test a large number of electrochemical reactions. These reactions
are categorized as competent or incompetent mixtures, and a classification
model was trained to predict reaction competency. This model is used
to screen 38,865 potential reactions in silico, and the predictions
are used to identify a number of reactions of synthetic or mechanistic
interest, 80% of which are found to be competent. Additionally, we
provide the predictions for the 38,865-member set in the hope of accelerating
the development of this field. We envision that adopting a workflow
such as this could enable the rapid development of many fields of
chemistry.