Loading…
Discovery of lead low-potential radical candidates for organic radical polymer batteries with machine-learning-assisted virtual screening
The discovery and development of new low reduction potential molecules that also have fast charge transfer kinetics is necessary for the further development of organic redox-active polymers in practical battery applications. Theoretical methods can aid in finding the lead radical candidates in large...
Saved in:
Published in: | Journal of materials chemistry. A, Materials for energy and sustainability Materials for energy and sustainability, 2022-04, Vol.1 (15), p.8273-8282 |
---|---|
Main Authors: | , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Summary: | The discovery and development of new low reduction potential molecules that also have fast charge transfer kinetics is necessary for the further development of organic redox-active polymers in practical battery applications. Theoretical methods can aid in finding the lead radical candidates in large initial screening spaces, but low-cost (yet accurate) methods are needed to predict the functional properties of the materials. In this paper, we conduct a two-objective (potential and dimer electronic coupling) virtual screening campaign to identify lead low potential candidate molecules in an initial space of 660 candidate molecules. The screening is accelerated by employing a trained Gaussian Process regression model for the voltage screening task. The model takes a combination of core-group chemical fingerprints and low-cost semi-empirical quantum chemistry calculations as the features for the model. The top-10%-predicted lowest reduction potential molecules of the initial space are then screened further to identify the candidates with the highest predicted electronic coupling. From the screening campaign, a set of promising redox-active molecules and two Pareto-optimal molecules (both
N
-methylphthalimides) are identified.
Lead candidate radicals for use in radical polymer batteries are discovered by virtual screening with low computational footprint, chemistry-informed machine learning methods. |
---|---|
ISSN: | 2050-7488 2050-7496 |
DOI: | 10.1039/d2ta00743f |