Neuro-Evolutive Modeling of Transition Temperatures for Five-Ring Bent-Core Molecules Derived from Resorcinol

Determining the phase transition temperature of different types of liquid crystals based on their structural parameters is a complex problem. The experimental work might be eliminated or reduced if prediction strategies could effectively anticipate the behavior of liquid crystalline systems. 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. 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. A number of 172 bent-core compounds with symmetrical calamitic wings were selected from the literature. All corresponding structures were fully optimized using the DFT, and the molecular descriptors were calculated afterward. In the first step, the ANN-DE approach predicted the mesophase presence for the analyzed compounds. Next, ANN models were determined to predict the transition temperatures and whether or not the bent-core compounds were mesogenic. Simple structural, thermophysical and electronic structure descriptors were considered as inputs in the dataset. As a result, the models determined for each individual temperature have an R-2 that varied from 0.89 to 0.98, indicating their capability to estimate the transition temperatures for the selected compounds. Moreover, the impact analysis of the inputs on the predicted temperatures showed that, in most cases, the presence or not of liquid crystalline properties represents the most influential feature.