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Computation 2016, 4(3), 25; doi:10.3390/computation4030025

Predictions of Physicochemical Properties of Ionic Liquids with DFT

1
Institute of Chemistry, University of Tartu, Ravila 14a, Tartu 50411, Estonia
2
Departamento de Química Física Aplicada, Universidad Autónoma de Madrid, Ciudad Universitaria de Cantoblanco, 28049 Madrid, Spain
*
Author to whom correspondence should be addressed.
Academic Editors: Karlheinz Schwarz and Agnes Nagy
Received: 31 May 2016 / Revised: 28 June 2016 / Accepted: 13 July 2016 / Published: 19 July 2016
View Full-Text   |   Download PDF [1380 KB, uploaded 19 July 2016]   |  

Abstract

Nowadays, density functional theory (DFT)-based high-throughput computational approach is becoming more efficient and, thus, attractive for finding advanced materials for electrochemical applications. In this work, we illustrate how theoretical models, computational methods, and informatics techniques can be put together to form a simple DFT-based throughput computational workflow for predicting physicochemical properties of room-temperature ionic liquids. The developed workflow has been used for screening a set of 48 ionic pairs and for analyzing the gathered data. The predicted relative electrochemical stabilities, ionic charges and dynamic properties of the investigated ionic liquids are discussed in the light of their potential practical applications. View Full-Text
Keywords: DFT; ionic liquids; high-throughput computation; electrochemical window; partial charge transfer; activation energy; viscosity DFT; ionic liquids; high-throughput computation; electrochemical window; partial charge transfer; activation energy; viscosity
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Karu, K.; Ruzanov, A.; Ers, H.; Ivaništšev, V.; Lage-Estebanez, I.; García de la Vega, J.M. Predictions of Physicochemical Properties of Ionic Liquids with DFT. Computation 2016, 4, 25.

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