Rapid and Accurate Prediction of the Melting Point for Imidazolium-Based Ionic Liquids by Artificial Neural Network - Xinyu Liu; Jie Yin; Xinmiao Zhang; Wenxiang Qiu; Wei Jiang; Ming Zhang; Linhua Zhu; Hongping Li; Huaming Li (Chemistry)
Imidazolium-based ionic liquids (ILs) have been regarded as green solvents owing to their unique properties. Among these, the melting point is key to their excellent performance in applications such as catalysis, biomass processing, and energy storage, where stability and operational temperature range are critical. The utilization of neural networks for forecasting the melting point is highly significant. Nevertheless, the excessive selection of descriptors obtained by density functional theory (DFT) calculations always leads to huge computational costs. Herein, this study strategically selected only 12 kinds of quantum chemical descriptors by employing a much more efficient semi-empirical method (PM7) to reduce computational costs. Four principles of data pre-processing were proposed, and the innovative use of a simulated annealing algorithm to search for the lowest energy molecular conformation improved accuracy. Based on these descriptors, a multi-layer perceptron neural network model was constructed to efficiently predict the melting points of 280 imidazolium-based ILs. The R2 value of the current model reached 0.75, and the mean absolute error reached 25.03 K, indicating that this study achieved high accuracy with very little computational cost. This study reveals a strong correlation between descriptors and melting points. Additionally, the model accurately predicts unknown melting points of imidazolium-based ILs, achieving good results efficiently.