Machine-Learning-Guided Discovery of Electrochemical Reactions
Name
zahrt-et-al-2022-machine-learning-guided-discovery-of-electrochemical-reactions.pdf
Description
Published version
Size
6.19 MB
Format
Adobe PDF
Checksum (MD5)
9f1d039668c4d180e0abae00d1a4994a
Author(s)
Zahrt, Andrew F
Mo, Yiming
Nandiwale, Kakasaheb Y
Shprints, Ron
Heid, Esther
Jensen, Klavs F
Date Issued
December 14, 2022
Journal
Journal of the American Chemical Society
Publisher
American Chemical Society
Citation
Andrew F. Zahrt, Yiming Mo, Kakasaheb Y. Nandiwale, Ron Shprints, Esther Heid, and Klavs F. Jensen. Journal of the American Chemical Society 2022 144 (49), 22599-22610.
Version
Final published version
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1021/jacs.2c08997
