液体和固体的电场驱动核动力学从一个多值机器学习的二极极模型
Elia Stocco1, Christian Carbogno2, Mariana Rossi1
1Max Planck Institute for the Structure and Dynamics of Matter, Hamburg, Germany.
概括
这项研究引入了一种新的机器学习方法,用于模拟电场驱动的分子动力学. 该方法准确地模拟了电场下的固体和液体中的材料特性和相变.
科学领域:
- 计算材料科学 计算材料科学
- 量子化学是一种量子化学.
- 机器学习应用程序 机器学习应用程序
背景情况:
- 外部电场可以诱导振动运动,使新材料相的探索成为可能.
- 精确模拟电场驱动动力学对于材料发现至关重要.
研究的目的:
- 开发一种通用,高效和准确的计算方法来模拟电场驱动的核动力学.
- 为了研究在电场相互作用下极性材料的性能变化.
主要方法:
- 在电双极近似中,结合了初始分子动力学与机器学习神经网络 (NN).
- 训练有素的等价和自差的NN对于原子间电位和双极,适应周期系统.
- 执行了长达纳秒的分子动力学模拟,具有量子力学准确性.
主要成果:
- 在电场下模拟极性液体 (水) 和极性固体 (LiNbO3) 的性能变化.
- 计算了液态水的介电函数 (GHz到THz),并观察到由于核量子效应而增强的电结.
- 在LiNbO3中模拟了铁电到电相变,观察驱动的声子模式,但没有完全极化开关.
结论:
- 开发的机器学习方法为研究电场驱动动力学提供了准确和高效的方法.
- 核量子效应在电场诱导的现象中发挥着重要作用,例如电结.
- 模拟揭示了电场下的铁电材料的复杂动态,突出了在模拟时间范围内实现完全偏振切换的局限性.
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