In silico prediction and experimental verification of ionic liquid refractive indices
| Autor: | Jaganathan Joshua Raj, Vishwesh Venkatraman, Kallidanthiyil Chellappan Lethesh, Anne Fiksdahl, Sigvart Evjen |
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| Rok vydání: | 2018 |
| Předmět: |
High-refractive-index polymer
Decision tree 02 engineering and technology 010402 general chemistry 021001 nanoscience & nanotechnology Condensed Matter Physics 01 natural sciences Atomic and Molecular Physics and Optics 0104 chemical sciences Electronic Optical and Magnetic Materials chemistry.chemical_compound chemistry Optical materials Ionic liquid Materials Chemistry Calibration Physical and Theoretical Chemistry 0210 nano-technology Biological system Refractive index Spectroscopy Energy (signal processing) Mathematics Test data |
| Zdroj: | Journal of Molecular Liquids. 264:563-570 |
| ISSN: | 0167-7322 |
| DOI: | 10.1016/j.molliq.2018.05.067 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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