TY - JOUR
T1 - Electro-Descriptors for the Performance Prediction of Electro-Organic Synthesis
AU - Chen, Yuxuan
AU - Tian, Bailin
AU - Cheng, Zheng
AU - Li, Xiaoshan
AU - Huang, Min
AU - Sun, Yuxia
AU - Liu, Shuai
AU - Cheng, Xu
AU - Li, Shuhua
AU - Ding, Mengning
N1 - Publisher Copyright:
© 2020 Wiley-VCH GmbH
PY - 2021/2/19
Y1 - 2021/2/19
N2 - Electrochemical organic synthesis has attracted increasing attentions as a sustainable and versatile synthetic platform. Quantitative assessment of the electro-organic reactions, including reaction thermodynamics, electro-kinetics, and coupled chemical processes, can lead to effective analytical tool to guide their future design. Herein, we demonstrate that electrochemical parameters such as onset potential, Tafel slope, and effective voltage can be utilized as electro-descriptors for the evaluation of reaction conditions and prediction of reactivities (yields). An “electro-descriptor-diagram” is generated, where reactive and non-reactive conditions/substances show distinct boundary. Successful predictions of reaction outcomes have been demonstrated using electro-descriptor diagram, or from machine learning algorithms with experimentally-derived electro-descriptors. This method represents a promising tool for data-acquisition, reaction prediction, mechanistic investigation, and high-throughput screening for general organic electro-synthesis.
AB - Electrochemical organic synthesis has attracted increasing attentions as a sustainable and versatile synthetic platform. Quantitative assessment of the electro-organic reactions, including reaction thermodynamics, electro-kinetics, and coupled chemical processes, can lead to effective analytical tool to guide their future design. Herein, we demonstrate that electrochemical parameters such as onset potential, Tafel slope, and effective voltage can be utilized as electro-descriptors for the evaluation of reaction conditions and prediction of reactivities (yields). An “electro-descriptor-diagram” is generated, where reactive and non-reactive conditions/substances show distinct boundary. Successful predictions of reaction outcomes have been demonstrated using electro-descriptor diagram, or from machine learning algorithms with experimentally-derived electro-descriptors. This method represents a promising tool for data-acquisition, reaction prediction, mechanistic investigation, and high-throughput screening for general organic electro-synthesis.
KW - electro-descriptors
KW - electrochemistry
KW - electroorganic synthesis
KW - machine learning
KW - reaction prediction
UR - http://www.scopus.com/inward/record.url?scp=85097940829&partnerID=8YFLogxK
U2 - 10.1002/anie.202014072
DO - 10.1002/anie.202014072
M3 - Article
C2 - 33180375
AN - SCOPUS:85097940829
SN - 1433-7851
VL - 60
SP - 4199
EP - 4207
JO - Angewandte Chemie - International Edition
JF - Angewandte Chemie - International Edition
IS - 8
ER -