A number of 172 bent-core compounds with symmetrical calamitic wings were selected from the literature. By these means, structural parameters such as the nature of the linking groups, the position, size and number of lateral substituents on the central core or calamitic wings and the length of the terminal chains were taken into account as factors that influence the liquid crystalline properties. Neuro-evolutive modeling based on artificial neural networks (ANN) and a differential evolution (DE) algorithm was applied to predict the phase transition temperatures of bent-core molecules based on their resorcinol core.
The experimental work might be eliminated or reduced if prediction strategies could effectively anticipate the behavior of liquid crystalline systems. Determining the phase transition temperature of different types of liquid crystals based on their structural parameters is a complex problem.
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