Predicting the Solubility of Organic Energy Storage Materials Based on Functional Group Identity and Substitution Pattern

Abstract

Organic electrode materials (OEMs) provide sustainable alternatives to conventional electrode materials based on transition metals. However, the application of OEMs in lithium-ion and redox flow batteries requires either low or high solubility. Currently, the identification of new OEM candidates relies on chemical intuition and trial-and-error experimental testing, which is costly and time intensive. Herein, we develop a simple empirical model that predicts the solubility of anthraquinones based on functional group identity and substitution pattern. Within this statistical scaffold, a training set of 18 anthraquinone derivatives allows us to predict the solubility of 808 quinones. Internal and external validations show that our model can predict the solubility of anthraquinones in battery electrolytes within log S ± 0.7, which is a much higher accuracy than existing solubility models. As a demonstration of the utility of our approach, we identified several new anthraquinones with low solubilities and successfully demonstrated their utility experimentally in Li-organic cells.

Data science enabled discovery of a highly soluble 2,2′-bipyrimidine anolyte for application in a flow battery

“…11 Since conventional redox flow batteries store energy in the form of dissolved redox active molecules, energy density is directly correlated to the charge carrier concentration. 3 The relationship between capacity and solubility has prompted efforts to understand the molecular features dictating solubility for flow battery materials, 12,13 as well as the introduction of an alternative configurations based on an insoluble charge storing species. 14 There are practical limitations to solubility, as the tradeoff between energy density, solution viscosity, and conductivity can become unfavorable at higher concentrations of active electrolyte in non-aqueous systems.…”

Long Cycle Life for Rechargeable Lithium Battery using Organic Small Molecule Dihydrodibenzo[c,h][2,6]naphthyridine‐5,11‐dione as a Cathode after Isoindigo Pigment Isomerization

“…Incorporating polarized groups, such as ‐CN, ‐OH, and ‐X (F, Cl, Br, I), into molecules has been proposed to reduce solubility and improve cycling stability. [ 9,38 ] Hence, fluorine substitution of isoindigo may hold promise for enhancing cycling stability through the hydro bond of imide group. In this study, we synthesized (E)−5,5′‐difluoro‐[3,3′‐biindolinylidene]−2,2′‐dione (EFID) and investigated its performance as a cathode material in LIBs.…”

Machine Learning Prediction of the Redox Activity of Quinones

“…A major disadvantage is that the density functional theory, which is comparatively computationally expensive, is used for descriptor computation. Tutte et al [21] propose a Hammet-like approach to model quinone solubility in organic electrolytes that are typically used in lithium-ion batteries (the organic electrode materials must have low solubility in the battery electrolyte). Machine learning screening has also been applied for the design of quinone electrolytes for redox flow batteries [22]: Wang et al created a dataset by generating various disubstituted quinones, replacing hydrogens in different quinone backbones with a predefined set of substituents, and, subsequently, utilized the extreme gradient boosting algorithm to build a model for screening the HOMO-LUMO gap and the free energy of solvation.…”