In silico prediction and experimental verification of ionic liquid refractive indices

Ionic liquids (ILs) have seen increasing use as environmentally friendly solvents in a wide array of applications from energy to pharmaceuticals. Among the many properties of interest, the refractive index, is of considerable importance since several related properties can be estimated once the refr...

Full description

Main Authors: Venkatraman, V., Raj, J.J., Evjen, S., Lethesh, K.C., Fiksdahl, A.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.20785 /
Published: Elsevier B.V. 2018
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047462903&doi=10.1016%2fj.molliq.2018.05.067&partnerID=40&md5=b8a546e17eee46a65cf01275cef400c5
http://eprints.utp.edu.my/20785/
Tags: Add Tag
No Tags, Be the first to tag this record!
id utp-eprints.20785
recordtype eprints
spelling utp-eprints.207852019-02-26T02:24:14Z In silico prediction and experimental verification of ionic liquid refractive indices Venkatraman, V. Raj, J.J. Evjen, S. Lethesh, K.C. Fiksdahl, A. Ionic liquids (ILs) have seen increasing use as environmentally friendly solvents in a wide array of applications from energy to pharmaceuticals. Among the many properties of interest, the refractive index, is of considerable importance since several related properties can be estimated once the refractive index of a material is known. Furthermore, high refractive index ILs are also used as reference solutions to determine properties of optical materials. However, with a large collection of cation-anion combinations to choose from, the task of finding suitable ionic liquids is far from trivial. In this article, machine learning models have been used to estimate the temperature-dependent refractive index over 450 diverse ILs using cheap to compute semi-empirically derived structure descriptors. In addition to using independent test sets for evaluating the predictive ability of the models, the efficacy of the models was further evaluated using 14 new ionic liquids that were synthesized. Overall, ensemble decision tree-based approaches gave the best results with mean absolute errors < 0.01 and squared correlations > 0.85 across both calibration and test data. © 2018 Elsevier B.V. Elsevier B.V. 2018 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047462903&doi=10.1016%2fj.molliq.2018.05.067&partnerID=40&md5=b8a546e17eee46a65cf01275cef400c5 Venkatraman, V. and Raj, J.J. and Evjen, S. and Lethesh, K.C. and Fiksdahl, A. (2018) In silico prediction and experimental verification of ionic liquid refractive indices. Journal of Molecular Liquids, 264 . pp. 563-570. http://eprints.utp.edu.my/20785/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description Ionic liquids (ILs) have seen increasing use as environmentally friendly solvents in a wide array of applications from energy to pharmaceuticals. Among the many properties of interest, the refractive index, is of considerable importance since several related properties can be estimated once the refractive index of a material is known. Furthermore, high refractive index ILs are also used as reference solutions to determine properties of optical materials. However, with a large collection of cation-anion combinations to choose from, the task of finding suitable ionic liquids is far from trivial. In this article, machine learning models have been used to estimate the temperature-dependent refractive index over 450 diverse ILs using cheap to compute semi-empirically derived structure descriptors. In addition to using independent test sets for evaluating the predictive ability of the models, the efficacy of the models was further evaluated using 14 new ionic liquids that were synthesized. Overall, ensemble decision tree-based approaches gave the best results with mean absolute errors < 0.01 and squared correlations > 0.85 across both calibration and test data. © 2018 Elsevier B.V.
format Article
author Venkatraman, V.
Raj, J.J.
Evjen, S.
Lethesh, K.C.
Fiksdahl, A.
spellingShingle Venkatraman, V.
Raj, J.J.
Evjen, S.
Lethesh, K.C.
Fiksdahl, A.
In silico prediction and experimental verification of ionic liquid refractive indices
author_sort Venkatraman, V.
title In silico prediction and experimental verification of ionic liquid refractive indices
title_short In silico prediction and experimental verification of ionic liquid refractive indices
title_full In silico prediction and experimental verification of ionic liquid refractive indices
title_fullStr In silico prediction and experimental verification of ionic liquid refractive indices
title_full_unstemmed In silico prediction and experimental verification of ionic liquid refractive indices
title_sort in silico prediction and experimental verification of ionic liquid refractive indices
publisher Elsevier B.V.
publishDate 2018
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047462903&doi=10.1016%2fj.molliq.2018.05.067&partnerID=40&md5=b8a546e17eee46a65cf01275cef400c5
http://eprints.utp.edu.my/20785/
_version_ 1741196396138070016
score 11.62408

Similar Items