Prediction of mesophase existence of liquid crystalline molecules based on their chemical structures applying Neuralnetwork

Abstract

Nematic liquid crystal is of significant industrial importance due to the potential for liquid crystal display (LCD) application. There are number of different display driving modes, and each of them requires different sort of liquid crystalline properties. In order to find out liquid crystals that are the most suitable for the applications, it should be important to know a correlation between the chemical structures and the properties. Among the properties, however, in every case, the most important and common demand is a wide and stable nematic phase. Up to now it has been recognized being extremely difficult to know exact chemical structures showing tendency to exhibit a nematic phase by other calculation methods i. e., molecular orbital calculations because the mesomorphic potential might be derived from the sensitive inter-molecular interaction.Applying Neural Network we have succeeded in development of a methodology estimating a relation between the chemical structures and the mesomorphic potential especially of the nematic phase. With 200 representative liquid crystals having wide variety of chemical structures, we have evaluated a relation of the chemical structures and the mesomorphic properties, and as the result the suitable Neural Network descriptors have been obtained. The developed methodology allows us to know if a chemical structure exhibits a nematic phase in 92% of accuracy without synthetic work.