Discovery of lead low-potential radical candidates for organic radical polymer batteries with machine-learning-assisted virtual screening. Issue 15 (18th March 2022)

• Record Type: Journal Article
• Authors: Li, Cheng-Han; Tabor, Daniel P.
• Is Part Of: Journal of materials chemistry. Volume 10:Issue 15(2022)
• Page Start: 8273
• Page End: 8282
• Publication Date: 2022-03-18
• Subjects: Materials -- Research -- Periodicals; Chemistry, Analytic -- Periodicals; Environmental sciences -- Research -- Periodicals; 543.0284
• DOI: 10.1039/d2ta00743f
• Languages: English
• ISSNs: 2050-7488

Abstract

Abstract : Lead candidate radicals for use in radical polymer batteries are discovered by virtual screening with low computational footprint, chemistry-informed machine learning methods. Abstract : 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.