The melting point (MP) of an ionic liquid (IL) is one of the key physical properties as it determines the lower limit of the IL working temperature range. In this work, we analysed the recently published studies to predict MP of ILs. While we were able to reproduce the statistical parameters reported by the authors, we found that the performance of the models with new test set data was much lower than the reported statistical values. The discrepancy was due to the validation protocol (random split of the initial set into training/test subsets) that did not allow correct estimation of how contributions of individual ions affect the model performance. Using a more rigorous validation protocol we reached good agreement between the training and test set statistical parameters. We strongly suggest using this protocol for proper validation of models for other properties of ILs to avoid reporting overoptimistic statistical parameters. We also showed that the Transformer Convolutional Neural Network, which was based on the representation of molecules as text (SMILES), proposed a model with significantly higher prediction accuracy as compared to those developed using descriptors that were used in the previous studies. The RMSE of this model is 44 degrees C and the model is applicable to any type of ILs. The data and developed models are publicly available online at http://ochem.eu/article/135195.
