Glass transition temperature of ionic liquids using molecular descriptors and artificial neural networks

Glass transition temperature data of ionic liquids (ILs) are analyzed to study the capabilities of artificial neural networks to correlate and predict this property. Molecular descriptors from computational chemistry are considered as independent variables to define the characteristics of an IL molecule. Several network architectures were considered, combinations of different descriptors were analyzed, and results were compared with other values reported in the literature. The independent variables (those that could have influence on the glass transition temperature) considered for training the artificial neural networks were (1) mass connectivity index λ, (2) cation mass M(+), (3) anion mass M(−), (4) surface area SA, (5) van der Waals volume Vw, (6) connectivity index X0, and (7) number of carbon atoms nC. The mass connectivity index is a parameter previously defined by the authors and is calculated for each IL, whereas the descriptors SA, Vw, X0, and nC were determined using the software Dragon7. As a measure of the accuracy of the method, the average relative deviation and the average relative absolute deviation are evaluated. Results of this work and others indicate that appropriate selection of data, good combination of architecture, and variables can lead to acceptable correlation of data but accurate prediction is not yet possible. The lack of a clear definition of the glass transition temperature and the lack of knowledge on what are the properties that most affect liquid-solid transition are the main causes of the present incapability for accurately predicting the glass transition temperature of the IL studied in this work.