Prediction of refractive index of binary solutions consisting of ionic liquids and alcohols (methanol or ethanol or 1-propanol) using artificial neural network

Soriano, Allan N.; Ornedo-Ramos, Karen Faith P.; Muriel, Carla Angela M.; Adornado, Adonis P.; Bungay, Vergel C.; Li, Menghui

doi:10.1016/j.jtice.2016.05.031

Cited by 14 publications

(6 citation statements)

References 38 publications

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“…Comparing the performance of our refractive index prediction approach with other published approaches 31,33,87 , we obtain similar or better relative deviations despite using a much simpler strategy. However, there are some differences that are worth to note.…”

Section: Prediction Of the Materials Dispersion Of Ilsmentioning

confidence: 59%

An experimental and computational study on the material dispersion of 1-alkyl-3-methylimidazolium tetrafluoroborate ionic liquids

Rodríguez-Fernández

Arosa

Algnamat

et al. 2020

Phys. Chem. Chem. Phys.

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Section: Prediction Of the Materials Dispersion Of Ilsmentioning

confidence: 59%

An experimental and computational study on the material dispersion of 1-alkyl-3-methylimidazolium tetrafluoroborate ionic liquids

Rodríguez-Fernández

Arosa

Algnamat

et al. 2020

Phys. Chem. Chem. Phys.

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“…Recently, researchers generally tend to expand their databases. Soriano et al 34 made use of a database including 752 data points of binary ILs in their ANN model with a mean absolute error of 0.00783 and an overall average percentage error of 0.55%. Mesbah et al 12 used 362 data points to model ternary systems through separate models: ANN and GEP.…”

Section: Introductionmentioning

confidence: 99%

Insights into modeling refractive index of ionic liquids using chemical structure-based machine learning methods

Esmaeili,

Hekmatmehr,

Atashrouz

et al. 2023

Sci Rep

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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.

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“…The artificial neural network finds initial applications in extracting both the refractive index and thickness of single-layer optical thin films in the visible region [ 19 , 20 , 21 ]. With the improvement of computer performance and the rise of many new disciplines and technologies, the use of neural network method in material characterization extends to quasi-crystalline alloy (Al 80 Mn 20 ) [ 22 ], silicon photonics [ 23 ], 2D materials (MoSe 2 , WS 2 , WSe 2 ) [ 24 ], 3D nanonetwork silicon structures [ 25 ], and binary ionic liquid system [ 26 , 27 ]. Recently, a new standard was proposed to evaluate the reliability of the optical parameter measurement of thin films via the neural network method [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

A General Neural Network Model for Complex Refractive Index Extraction of Low-Loss Materials in the Transmission-Mode THz-TDS

Zhou

Jia

Cao

2022

Sensors

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The complex refractive index for low-loss materials is conventionally extracted by either approximate analytical formula or numerical iterative algorithm (such as Nelder-Mead and Newton-Raphson) based on the transmission-mode terahertz time domain spectroscopy (THz-TDS). A novel 4-layer neural network model is proposed to obtain optical parameters of low-loss materials with high accuracy in a wide range of parameters (frequency and thickness). Three materials (TPX, z-cut crystal quartz and 6H SiC) with different dispersions and thicknesses are used to validate the robustness of the general model. Without problems of proper initial values and non-convergence, the neural network method shows even smaller errors than the iterative algorithm. Once trained and tested, the proposed method owns both high accuracy and wide generality, which will find application in the multi-class object detection and high-precision characterization of THz materials.

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Prediction of refractive index of binary solutions consisting of ionic liquids and alcohols (methanol or ethanol or 1-propanol) using artificial neural network

Cited by 14 publications

References 38 publications

An experimental and computational study on the material dispersion of 1-alkyl-3-methylimidazolium tetrafluoroborate ionic liquids

An experimental and computational study on the material dispersion of 1-alkyl-3-methylimidazolium tetrafluoroborate ionic liquids

Insights into modeling refractive index of ionic liquids using chemical structure-based machine learning methods

A General Neural Network Model for Complex Refractive Index Extraction of Low-Loss Materials in the Transmission-Mode THz-TDS

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