Predicting refractive index of ionic liquids based on the extreme learning machine (ELM) intelligence algorithm

Abstract Refractive index is an important factor that determines the purity and concentration of a substance, but large sample sizes and the low efficiencies of traditional measurement methods and algorithms have led to a lack of refractive index data. To identify and control the performance of materials in chemistry and engineering, detailed refractive-index information for all substances is vital. In this study, the extreme learning machine (ELM) intelligence algorithm and a multiple linear regression (MLR) approach were used to predict the refractive index of ionic liquids (ILs) from molecular descriptors calculated by quantum chemistry. We collected 1194 refractive index data points for 115 ILs at different temperatures from 112 studies, where the average absolute relative deviation (AARD%) for the entire set was 0.855% for MLR and 0.295% for ELM. Such reliable and accurate results highlight the potential of the ELM and MLR models for predicting the refractive index and other related physical parameters of a material.