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Insights into modeling refractive index of ionic liquids using chemical structure-based machine learning methods.
Esmaeili, Ali; Hekmatmehr, Hesamedin; Atashrouz, Saeid; Madani, Seyed Ali; Pourmahdi, Maryam; Nedeljkovic, Dragutin; Hemmati-Sarapardeh, Abdolhossein; Mohaddespour, Ahmad.
Afiliação
  • Esmaeili A; Renewable Energies Engineering Department, Faculty of Mechanical and Energy Engineering, Shahid Beheshti University, Tehran, Iran.
  • Hekmatmehr H; Renewable Energies Engineering Department, Faculty of Mechanical and Energy Engineering, Shahid Beheshti University, Tehran, Iran.
  • Atashrouz S; Department of Chemical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran. s.atashrouz@gmail.com.
  • Madani SA; Department of Chemical and Petroleum Engineering, University of Calgary, 2500 University Drive NW, Calgary, AB, T2N 1N4, Canada.
  • Pourmahdi M; Department of Polymer Reaction Engineering, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran.
  • Nedeljkovic D; College of Engineering and Technology, American University of the Middle East, Egaila, 54200, Kuwait.
  • Hemmati-Sarapardeh A; Department of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman, Iran. hemmati@uk.ac.ir.
  • Mohaddespour A; State Key Laboratory of Continental Shale Hydrocarbon Accumulation and Efficient Development, Ministry of Education, Northeast Petroleum University, Daqing, 163318, China. hemmati@uk.ac.ir.
Sci Rep ; 13(1): 11966, 2023 Jul 24.
Article em En | MEDLINE | ID: mdl-37488224
ABSTRACT
Ionic liquids (ILs) have drawn much attention due to their extensive applications and environment-friendly nature. Refractive index prediction is valuable for ILs quality control and property characterization. This paper aims to predict refractive indices of pure ILs and identify factors influencing refractive index changes. Six chemical structure-based machine learning models called eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Categorical Boosting (CatBoost), Convolutional Neural Network (CNN), Adaptive Boosting-Decision Tree (Ada-DT), and Adaptive Boosting-Support Vector Machine (Ada-SVM) were developed to achieve this goal. An enormous dataset containing 6098 data points of 483 different ILs was exploited to train the machine learning models. Each data point's chemical substructures, temperature, and wavelength were considered for the models' inputs. Including wavelength as input is unprecedented among predictions done by machine learning methods. The results show that the best model was CatBoost, followed by XGBoost, LightGBM, Ada-DT, CNN, and Ada-SVM. The R2 and average absolute percent relative error (AAPRE) of the best model were 0.9973 and 0.0545, respectively. Comparing this study's models with the literature shows two advantages regarding the dataset's abundance and prediction accuracy. This study also reveals that the presence of the -F substructure in an ionic liquid has the most influence on its refractive index among all inputs. It was also found that the refractive index of imidazolium-based ILs increases with increasing alkyl chain length. In conclusion, chemical structure-based machine learning methods provide promising insights into predicting the refractive index of ILs in terms of accuracy and comprehensiveness.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Sci Rep Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Irã

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Sci Rep Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Irã
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