# Machine-Learning Model Prediction of Ionic Liquids Melting Points
## 2 Methods  
The data used in this study were collected from the Ionic Liquids Database--ILThermo (v2.0) ([[https://ilthermo.boulder.nist.gov/](https://ilthermo.boulder.nist.gov/)]([https://ilthermo.boulder.nist.gov/](https://ilthermo.boulder.nist.gov/))) (accessed on 25 October 2021) [31; 32]. According to the latest updates of this database, it contains 2175 types of ILs that comprises about 4200 compounds compiled from nearly 3500 published references. For pure ionic liquids alone, the database contains nearly 1800 IL systems. For the datapoints related to pure ILs, there are nearly 120,000 datapoints available. These datapoints cover various aspects in thermodynamic, thermochemical and transport properties. The melting points data we collected, which contain 1253 reported ILs, covers a large variety of ILs families. For such a large collection, predicting melting points accurately without using costly computational resources is highly desirable. Figure 1 provides a schematic overview of the workflow we adopted in this work to predict the melting points (\(T_{m}\)) of ILs based on the published dataset in ILThermo database  
using a machine-learning (ML) model. As an initial step, we extracted the physical and chemical properties in which we were interested, i.e., melting temperature (\(T_{m}\)), from the ILThermo database by utilizing pyilt2 library [33] with our in-house code written in python. For the conversion of IUPAC names of ILs to SMILES representations, the functionality in the OPSIN library [34] was used. Based on the Dragon7 software [35], there were 5272 molecular descriptors based on a quantitative structure-activity relationship (QSPR) calculated for each ionic liquid molecule in the dataset. These molecular descriptors were then subsequently analyzed using statistical and machine-learning (ML) models based on the python libraries available in scikit-learn (scikit-learn 1.0.2), TensorFlow (TensorFlow 2) and Keras (Keras 2.7) [36; 37; 38]. Prior to ML modeling, all the low variance molecular descriptors columns and those containing missing values or empty columns were excluded. To prevent overfitting, we further reduced the dimensionality of the original descriptor matrix, and the Pearson correlation matrix was used to identify a set of significant molecular descriptors that show a statistical significance with high correlation in determining the melting points of ILs. By excluding the molecular descriptors with low correlation (<0.20) and high correlation (>0.90), a set of important molecular features which consists of 137 molecular descriptors were identified and were used to fit the ML model. After the correlation features selection and normalization of molecular descriptors, the dataset was randomly divided into training (80%) and testing or validation (20%) data for the validation of the ML model's prediction. In this study, our primary structure-property predictive machine-learning model is based on a deep learning (DL) model composed of recurrent and recursive neural networks, RNNs [25; 26; 39], which are a family of neural networks specialized to process sequential data. A deep learning model is a subset of machine-learning models [25; 26; 27; 28; 39] which mimics how the human brain processes information and learns based on a set of algorithms which 'learns in layers'. It involves learning through layers which allows a computer to develop a hierarchy of learning process by developing several layers of information processing states in hierarchical structures to learn and infer. A typical DL model contains multiple hidden layers including input and output layers represented by neural networks. In terms of implementing and training the DL model, it relies on parallelized matrix and tensor operations, as well as computing gradients and optimization [25; 26; 27; 28; 39]. Thus, to construct this DL model, the libraries and utilities including pre-trained models available in Keras and TensorFlow were used.