Molecular Charge Topologies Govern Polar Nematic Ordering.

Area of Science:

• Materials Science
• Condensed Matter Physics
• Computational Chemistry

Background:

• Polar nematic phases possess spontaneous macroscopic polarization alongside conventional nematic orientational order.
• These phases are promising for developing responsive optical and electronic materials.
• Identifying the molecular factors driving polar order is challenging, as current methods lack predictive power.

Purpose of the Study:

• To investigate the molecular determinants of spontaneous polarization in nematic liquid crystals.
• To develop a reliable method for distinguishing polar and apolar nematic compounds.
• To identify topological motifs governing spontaneous polarization.

Main Methods:

• Analysis of charge topologies for 236 nematic compounds using σ-profiles and charge-weighted Graph Fourier Transforms (GFT).
• Development and application of a machine learning classification model.
• Evaluation of the model's performance against traditional metrics like dipole moment and σ-profiles.

Main Results:

• A single order parameter derived from GFT accurately classifies nematic compounds as polar or apolar.
• The GFT-based machine learning approach outperforms traditional metrics in distinguishing polar and apolar nematics.
• The study identifies key topological motifs responsible for spontaneous polarization.

Conclusions:

• The developed GFT-based machine learning framework offers a physically interpretable and computationally efficient method for predicting polar nematic behavior.
• This approach advances the rational design of novel materials with desired polar properties.
• Understanding molecular topology is key to controlling spontaneous polarization in liquid crystals.