Design of Thermotropic Liquid Crystal Molecules With a Wide Temperature Range for the Liquid Crystal Phase Using Machine Learning

Thermotropic liquid crystals are functional materials whose phase structures change with temperature. They exhibit mesophases—intermediate states between solid and liquid. There is demand for materials that maintain a stable mesophase over a wide temperature range Δ T LC , enabling operation even under harsh conditions. Conventional liquid crystal development requires significant time and money from molecular design to physical property evaluation. This study designs liquid crystal molecules exhibiting wide Δ T LC in a specific mesophase. The proposed method enables efficient liquid crystal exploration by predicting Δ T LC for new materials using machine learning. First, it identifies the mesophase formed from the molecular structure. Next, it predicts the phase transition temperatures (melting point and clearing point) for that phase. Inputting molecules with unknown Δ T LC into these models enables the prediction of Δ T LC for any given mesophase and efficient exploration of liquid crystal molecules.