Transferability of the chemical-bond-based machine learning model for dipole moment: The GHz to THz dielectric properties of liquid propylene glycol and polypropylene glycol

We conducted a first-principles study of the dielectric properties of liquid propylene glycol (PG) and polypropylene glycol (PPG) using a recently developed chemical-bond-based machine learning (ML) model for dipole moments [T. Amano et al., Phys. Rev. B 110, 165159 (2024)]. The ML dipole models successfully predict the dipole moment of various liquid configurations in close agreement with density functional theory calculations, and they generate $20\phantom{\rule{0.16em}{0ex}}\mathrm{n}\mathrm{s}$ quantum-accuracy dipole moment trajectories to calculate the dielectric function, when combined with ML potentials. The calculated dielectric function of PG closely matches experimental results. We identified a libration peak at $600\phantom{\rule{0.16em}{0ex}}{\mathrm{cm}}^{\ensuremath{-}1}$ and an intermolecular mode at $100\phantom{\rule{0.16em}{0ex}}{\mathrm{cm}}^{\ensuremath{-}1}$, previously noted experimentally. Furthermore, the models trained on PG2 training data can apply to longer chain PPGs not included in the training data. The present research marks the first step toward developing a universal bond-based dipole model.