Molecular Charge Topologies Govern Polar Nematic Ordering

Polar nematic phases exhibit spontaneous macroscopic polarization while retaining the orientational order of conventional nematics, offering opportunities for responsive optical and electronic materials. The molecular determinants of polar order remain elusive, as traditional design strategies focus on the magnitude of the dipole moment without reliable prediction. We analyze charge topologies of a curated data set of 236 nematic compounds by computing their σ-profiles and charge-weighted Graph Fourier Transforms (GFT). We show that machine learning classification using a single order parameter derived from GFT can accurately distinguish polar and apolar nematic formers and outperforms dipolar and σ-profile metrics. This framework provides a physically interpretable and computationally lightweight approach capable of identifying topological motifs governing spontaneous polarization.