Dypvik Sødahl Elin, Carrete Jesús, Madsen Georg K H, Berland Kristian
Department of Mechanical Engineering and Technology Management, Norwegian University of Life Sciences, N-1433 AS, Norway.
Instituto de Nanociencia y Materiales de Aragón (INMA), CSIC-Universidad de Zaragoza, ES-50009 Zaragoza, Spain.
J Phys Chem C Nanomater Interfaces. 2024 Dec 18;129(1):484-494. doi: 10.1021/acs.jpcc.4c06615. eCollection 2025 Jan 9.
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 HdabcoClO 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 phase transition coinciding with the orientational disorder of ClO and the onset of rotation of both Hdabco and ClO 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.
具有取向无序中间相的混合分子铁电体作为传统无机铁电体的无铅替代品具有巨大潜力，这归因于其室温铁电性、低能合成、可塑性以及多轴极化潜力等特性。铁电分子盐HdabcoClO因其超快的铁电室温开关特性而备受关注。然而，到目前为止，对这些化合物中出现的动态无序的本质了解有限。在这里，我们使用神经网络NeuralIL，利用密度泛函理论生成的训练数据来训练机器学习力场（MLFF）。所得的MLFF-MD模拟显示出与早期报道的实验结果一致的相变和热膨胀，这既包括与ClO的取向无序同时发生的低温相变，也包括高温相变中Hdabco和ClO两者旋转的开始。我们还发现即使在低温相中也存在质子转移，其随温度升高而增加，并导致相关的质子无序以及氢键链方向上无序的开始。
