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

TL;DR

This study demonstrates that a chemical bond-based machine learning model for dipole moments can accurately predict dielectric properties of liquids like propylene glycol and polypropylene glycol across GHz to THz frequencies, matching experimental data.

Contribution

It introduces a transferable ML dipole model trained on PG data that successfully applies to longer-chain PPG, advancing toward a universal bond-based dipole model.

Findings

ML model predicts dipole moments with close DFT agreement

Calculated dielectric functions match experimental results

Model trained on PG data applies to PPG chains not in training set

Abstract

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 DFT calculations and generate $20 \mathrm{ns}$ 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 $600\, \mathrm{cm}^{-1}$ and an intermolecular mode at $100\, \mathrm{cm}^{-1}$, previously noted experimentally. Furthermore, the models trained on PG2 training data can apply to longer chain PPG not included in the training data. The present research marks the first step toward developing a universal bond-based dipole model.