Abstract
Liquid crystal (LC) phases must exist in an appropriate temperature range for practical applications and thus the melting point (MP) is a critical design target of LCs. In this work, we have evaluated the performance of directed message passing neural networks trained on a large database of organic molecules (27,000+) in the prediction of a structurally diverse set of 780 LC and LC-like molecules including cyano-, azo-, ester, cyclohexyl- and halogen-containing compounds. Our results show that the model can provide accurate MP predictions for LC and LC-like molecules with an overall Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of 23°C and 30°C, respectively. Our model provides similar levels of performance with MAEs of 20°C, 20°C, 23°C, 25°C and 24°C for azo, cyclohexyl, ester, halogen and nitrile compounds, respectively, and is most accurate for molecules with MP between 50 and 150°C (MAE = 21°C). We have developed an online tool called LCMelt (v1.0) (lcmelt.streamlit.app) where our model can be used to predict the MP of potential LC candidates before synthesis free of charge.