Dynamical Disorder in the Mesophase Ferroelectric HdabcoClO4: A Machine-Learned Force Field Study

TL;DR

This study uses machine learning-based force fields to simulate and understand the dynamical disorder and phase transitions in the ferroelectric compound HdabcoClO4, revealing proton transfer and molecular rotations across phases.

Contribution

It introduces a neural network-trained force field for accurate MD simulations of HdabcoClO4, capturing phase transitions and dynamical disorder phenomena.

Findings

MLFF reproduces experimental phase transitions

Proton transfer increases with temperature

Dynamical disorder involves molecular rotations and proton movement

Abstract

Hybrid molecular ferroelectrics with orientationally disordered mesophases offer significant promise as lead-free alternatives to traditional inorganic ferroelectrics owing to properties such as room temperature ferroelectricity, low-energy synthesis, malleability, and potential for multiaxial polarization. The ferroelectric molecular salt HdabcoClO4 is of particular interest due to its ultrafast ferroelectric room-temperature switching. However, so far, there is limited understanding of the nature of dynamical disorder arising in these compounds. Here, we employ the neural network NeuralIL to train a machine-learned force field (MLFF) with training data generated using density functional theory. The resulting MLFF-MD simulations exhibit phase transitions and thermal expansion in line with earlier reported experimental results, for both a low-temperature phasetransition coinciding with the orientational disorder of ClO4- molecules and the onset of rotation of Hdabco+ and ClO4- molecules in a high-temperature phase transition. We also find proton transfer even in the low-temperature phase, which increases with temperature and leads to associated proton disorder as well as the onset of disorder in the direction of the hydrogen-bonded chains.