Prediction of clearing temperatures of bent-core liquid crystals using decision trees and multivariate adaptive regression splines
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2016
Authors
Antanasijević, JelenaPocajt, Viktor
Antanasijević, Davor
Trišović, Nemanja
Fodor-Csorba, Katalin
Article (Published version)
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Show full item recordAbstract
Accurate prediction of transition temperature is very helpful for the design of new liquid crystals (LCs) because even small changes in structure can dramatically alter the transition temperature, and therefore the synthesis of LCs should not be governed only by chemical intuition. A quantitative structure-property relationship (QSPR) study was performed on 243 five-ring bent-core LCs in order to predict their clearing temperatures using molecular descriptors. Decision tree and multivariate adaptive regression splines (MARS), techniques well suited for high-dimensional data analysis, were applied to select important descriptors (dimension reduction) and to generate nonlinear models. These techniques were applied both on two-dimensional (2D) descriptors only and on the pool of 2D and 3D descriptors (2& 3D). The obtained QSPR models were tested using 15% of available data, and their performance and ability to generalise were analysed using multiple statistical metrics. The best results f...or the external test set were obtained using the MARS model created with 2& 3D descriptors, with a high correlation coefficient of r = 0.95 and a root mean squared error of 7.41 K. All metrics suggest that the proposed QSPR model, generated by MARS, is a robust and satisfactorily accurate approach for the prediction of clearing temperatures of bent-core LCs. [GRAPHICS] .
Keywords:
QSPR / MARS modelling / decision tree / transition temperatureSource:
Liquid Crystals, 2016, 43, 8, 1028-1037Publisher:
- Taylor & Francis Ltd, Abingdon
Funding / projects:
- Development and Application of Methods and Materials for Monitoring New Organic Contaminants, Toxic Compounds and Heavy Metals (RS-172007)
- Study of the Synthesis, Structure and Activity of Natural and Synthetic Organic Compounds (RS-172013)
DOI: 10.1080/02678292.2016.1155769
ISSN: 0267-8292
WoS: 000378080600002
Scopus: 2-s2.0-84961215303
Institution/Community
Tehnološko-metalurški fakultetTY - JOUR AU - Antanasijević, Jelena AU - Pocajt, Viktor AU - Antanasijević, Davor AU - Trišović, Nemanja AU - Fodor-Csorba, Katalin PY - 2016 UR - http://TechnoRep.tmf.bg.ac.rs/handle/123456789/3284 AB - Accurate prediction of transition temperature is very helpful for the design of new liquid crystals (LCs) because even small changes in structure can dramatically alter the transition temperature, and therefore the synthesis of LCs should not be governed only by chemical intuition. A quantitative structure-property relationship (QSPR) study was performed on 243 five-ring bent-core LCs in order to predict their clearing temperatures using molecular descriptors. Decision tree and multivariate adaptive regression splines (MARS), techniques well suited for high-dimensional data analysis, were applied to select important descriptors (dimension reduction) and to generate nonlinear models. These techniques were applied both on two-dimensional (2D) descriptors only and on the pool of 2D and 3D descriptors (2& 3D). The obtained QSPR models were tested using 15% of available data, and their performance and ability to generalise were analysed using multiple statistical metrics. The best results for the external test set were obtained using the MARS model created with 2& 3D descriptors, with a high correlation coefficient of r = 0.95 and a root mean squared error of 7.41 K. All metrics suggest that the proposed QSPR model, generated by MARS, is a robust and satisfactorily accurate approach for the prediction of clearing temperatures of bent-core LCs. [GRAPHICS] . PB - Taylor & Francis Ltd, Abingdon T2 - Liquid Crystals T1 - Prediction of clearing temperatures of bent-core liquid crystals using decision trees and multivariate adaptive regression splines EP - 1037 IS - 8 SP - 1028 VL - 43 DO - 10.1080/02678292.2016.1155769 ER -
@article{ author = "Antanasijević, Jelena and Pocajt, Viktor and Antanasijević, Davor and Trišović, Nemanja and Fodor-Csorba, Katalin", year = "2016", abstract = "Accurate prediction of transition temperature is very helpful for the design of new liquid crystals (LCs) because even small changes in structure can dramatically alter the transition temperature, and therefore the synthesis of LCs should not be governed only by chemical intuition. A quantitative structure-property relationship (QSPR) study was performed on 243 five-ring bent-core LCs in order to predict their clearing temperatures using molecular descriptors. Decision tree and multivariate adaptive regression splines (MARS), techniques well suited for high-dimensional data analysis, were applied to select important descriptors (dimension reduction) and to generate nonlinear models. These techniques were applied both on two-dimensional (2D) descriptors only and on the pool of 2D and 3D descriptors (2& 3D). The obtained QSPR models were tested using 15% of available data, and their performance and ability to generalise were analysed using multiple statistical metrics. The best results for the external test set were obtained using the MARS model created with 2& 3D descriptors, with a high correlation coefficient of r = 0.95 and a root mean squared error of 7.41 K. All metrics suggest that the proposed QSPR model, generated by MARS, is a robust and satisfactorily accurate approach for the prediction of clearing temperatures of bent-core LCs. [GRAPHICS] .", publisher = "Taylor & Francis Ltd, Abingdon", journal = "Liquid Crystals", title = "Prediction of clearing temperatures of bent-core liquid crystals using decision trees and multivariate adaptive regression splines", pages = "1037-1028", number = "8", volume = "43", doi = "10.1080/02678292.2016.1155769" }
Antanasijević, J., Pocajt, V., Antanasijević, D., Trišović, N.,& Fodor-Csorba, K.. (2016). Prediction of clearing temperatures of bent-core liquid crystals using decision trees and multivariate adaptive regression splines. in Liquid Crystals Taylor & Francis Ltd, Abingdon., 43(8), 1028-1037. https://doi.org/10.1080/02678292.2016.1155769
Antanasijević J, Pocajt V, Antanasijević D, Trišović N, Fodor-Csorba K. Prediction of clearing temperatures of bent-core liquid crystals using decision trees and multivariate adaptive regression splines. in Liquid Crystals. 2016;43(8):1028-1037. doi:10.1080/02678292.2016.1155769 .
Antanasijević, Jelena, Pocajt, Viktor, Antanasijević, Davor, Trišović, Nemanja, Fodor-Csorba, Katalin, "Prediction of clearing temperatures of bent-core liquid crystals using decision trees and multivariate adaptive regression splines" in Liquid Crystals, 43, no. 8 (2016):1028-1037, https://doi.org/10.1080/02678292.2016.1155769 . .