A novel dataset of helical twisting power of chiral dopants in nematic liquid crystals phase

The dataset is collected manually using the experimental molar helical twisting power, nematic host, chiral dopant, molar concentration of the dopant, and temperature. The dataset consists of 527 experimental measurements of helical twisting powers, optimised using molecular force fields in RDKit. The bootstrapping method was employed to generate of about 30 conformers for each dopant using RDKit. In total, the database has about 15000 data points. The chiral descriptors were developed for use in machine learning models for each data entry point. Value of the Data • This is the first available dataset of the helical twisting power (HTP) of chiral dopants in nematic liquid crystals. • The dataset can be reused for machine learning predictions of HTP. • The dataset can be reused for generating efficient chiral descriptors of dopant molecules to be utilised with machine learning models. • The dataset can be reused for predicting new chiral dopant molecules with specific helical twisting power. • The dataset can be reused to add new chiral dopant molecules with known experimental helical twisting powers in various nematic liquid crystals.