在应变PbTiO3中诱导和铁电切换流闭域与神经网络量子分子动力学的神经网络
Thomas M Linker1,2, Ken-Ichi Nomura1, Shogo Fukushima3
1Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, California 90089-0242, United States.
我们开发了NNQMD-BEC,这是一种用于铁电领域电场动态的新模拟方法. 它揭示了应变材料中的节能切换路径,有助于设备工程.
科学领域:
- 计算材料科学科学 计算材料科学
- 凝聚物质物理学 凝聚物质物理学
- 量子化学 是一个量子化学.
背景情况:
- 神经网络量子分子动力学 (NNQMD) 是一个强大的模拟工具.
- 了解铁电域壁 (DW) 切换对于设备应用至关重要.
- 整合电场动力学需要先进的模拟方法.
研究的目的:
- 将NNQMD扩展到使用BEC的电场动态.
- 为了验证新的NNQMD-BEC方法用于铁电切换.
- 为了研究电场诱导的现象在张力铁电材料.
主要方法:
- 开发了NNQMD-BEC模拟方法.
- 验证了使用酸 (PbTiO3) 大量和域壁的方法.
- 在电场下模拟三轴应力PbTiO3.
主要成果:
- NNQMD-BEC准确地描述了铁电领域壁切换机制.
- 电场的应用在应力PbTiO3.3中诱导了流量闭合纹理.
- 场逆转显示了通过短暂流的能效切换.
结论:
- NNQMD-BEC为研究电机效应提供了一个理论框架.
- 该方法将机器学习力场与BEC集成在一起.
- 这些发现与工程领域控制的铁电器件开关有关.
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