机器学习加速的第一原则 精确建模在地幔条件下的MGO固体-液体相变的准确建模
Pandu Wisesa1, Christopher M Andolina1, Wissam A Saidi1
1Department of Mechanical Engineering and Materials Science, University of Pittsburgh, Pittsburgh, Pennsylvania 15216, United States.
The journal of physical chemistry letters
|September 22, 2023
概括
深度神经网络潜力加速了对氧化 (MgO) 的高压化行为研究. 这种方法准确地模拟行星状况,克服了传统计算技术的局限性.
科学领域:
- 材料科学 材料科学 材料科学
- 地质物理学 地质物理学
- 计算物理 计算物理
背景情况:
- 在极端高压条件下精确测量氧化 (MgO) 对于了解行星内部至关重要.
- 实验和计算建模对在这些条件下研究MgO提出了重大挑战.
研究的目的:
- 为了加速密度函数理论 (DFT) 对MGO的计算,使用深度神经网络潜力 (DNP).
- 使用双相共存 (TPC) 方法研究MgO在高压 (0-300 GPa) 和高温 (≤9600 K) 的化行为.
主要方法:
- 在多个MgO阶段开发和训练深度神经网络潜力 (DNP).
- 使用双相共存 (TPC) 方法与DNP加速计算.
- 利用大规模的分子动力学模拟 (∼16000个原子,>100 psi) 来减轻有限大小的影响.
主要成果:
- 的DNP-TPC化曲线与现有的实验数据显示出极好的一致性.
- 证明了大规模模型的必要性,以准确预测融温度,减轻显著的有限尺寸效应.
- 展示了DNP在高压下准确描述MgO金属化的能力,这是古典潜能所错过的现象.
结论:
- 深度神经网络潜力为模拟高压材料行为提供了可行且准确的方法.
- 这项研究为MgO提供了一个可靠的化曲线,与行星科学相关.
- 先进的计算方法对于克服研究极端物质条件的局限性至关重要.
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