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Deep eutectic solvent viscosity prediction by hybrid machine learning and group contribution
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Orientador(es)
Resumo(s)
In this study, hybrid machine learning nonlinear models were developed to predict the viscosity of DESs by combining the group contribution (GC) concept with the multilayer perceptron, a well-known feedforward artificial neural network, and the Least Squares Support Vector Machine (LSSVM) algorithm. Deep Eutectic Solvents (DESs) have come to the forefront in recent years as appealing substitutes for conventional solvents. It is imperative to have a thorough grasp of the essential properties of DESs to expand the employment of these compounds in new procedures. Most frequently, one of the crucial physical properties of a DES that must be precisely determined is its viscosity. To develop the models, a dataset of 2533 viscosity data points for 305 DESs at various temperatures (from 277.15 to 373.15 K) was gathered to build the models. By using temperature, molar ratios, and functional groups as inputs, the results indicate that the suggested models can determine the viscosity of DESs with high accuracy. The models yield average absolute relative deviations below 10% and squared correlation coefficients higher than 0.98.
Descrição
Funding Information: The authors wish to thank Shiraz University, University of Isfahan and Universidade Nova de Lisboa for the facilities provided. Publisher Copyright: © 2023 The Author(s)
Palavras-chave
Artificial neural network DES Machine learning Physical property Support vector machine Electronic, Optical and Magnetic Materials Atomic and Molecular Physics, and Optics Condensed Matter Physics Spectroscopy Physical and Theoretical Chemistry Materials Chemistry