title: Screening for High Conductivity/Low Viscosity Ionic Liquids Using Product Descriptors.
abstract: We seek to optimize Ionic liquids (ILs) for application to redox flow batteries. As part of this effort, we have developed a computational method for suggesting ILs with high conductivity and low viscosity. Since ILs consist of cation-anion pairs, we consider a method for treating ILs as pairs using product descriptors for QSPRs, a concept borrowed from the prediction of protein-protein interactions in bioinformatics. We demonstrate the method by predicting electrical conductivity, viscosity, and melting point on a dataset taken from the ILThermo database on June 18th, 2014. The dataset consists of 4,329 measurements taken from 165 ILs made up of 72 cations and 34 anions. We benchmark our QSPRs on the known values in the dataset then extend our predictions to screen all 2,448 possible cation-anion pairs in the dataset.
authors.affiliation: Sandia National Laboratories, Albuquerque, New Mexico, 87185, USA.
authors.forename: Shawn
authors.initials: S
authors.lastname: Martin
authors.affiliation: Sandia National Laboratories, Albuquerque, New Mexico, 87185, USA.
authors.forename: Harry D
authors.initials: HD
authors.lastname: Pratt
authors.affiliation: Sandia National Laboratories, Albuquerque, New Mexico, 87185, USA.
authors.forename: Travis M
authors.initials: TM
authors.lastname: Anderson
mesh_terms: D004553:Electric Conductivity; D052578:Ionic Liquids; D008962:Models, Theoretical; D013696:Temperature; D014783:Viscosity
chemical_list: D052578:Ionic Liquids
keywords: Conductivity; ILThermo Database; Ionic Liquids; Melting Point; Product Descriptors; Quantitative Structure Property Relationships; Redox Flow Batteries; Viscosity