对CaO3的化曲线和相位图的研究,使用深度学习潜力
Shuai-Jun Liu1, Zhen-Shuai Lei2, Meng-Ru Chen3
1School of Mathematics and Physics, Lanzhou Jiaotong University, Lanzhou 730070, China.
The journal of physical chemistry. A
|October 18, 2024
概括
本研究使用深度学习潜力模拟酸 (CaO3) 的化曲线和相位图. 结果揭示了关于下层地幔矿物行为的关键见解和行星内部动态.
科学领域:
- 行星科学 行星科学
- 地质物理学 地质物理学
- 材料科学 材料科学 材料科学
背景情况:
- 深层行星融化影响核心形成,磁场和地的演变.
- 了解行星内部需要了解深层地质过程.
研究的目的:
- 研究CaO3的化曲线和P-T相图,CaO3是下地幔矿物质的候选矿物.
- 使用深度学习潜力模型进行准确的模拟.
主要方法:
- 采用了第一原理,分子动力学和准和近似.
- 经过验证的深度学习潜力模型,使用状态方程和阶段过渡压力的方程.
- 使用单相,空气和双相方法计算化温度.
主要成果:
- 确定的零压融温度为975K (单相),850K (真空) 和755K (双相).
- 通过辐射分布函数和平均平方位移分析了融过程.
- 使用双相方法生成高达135 GPa的CaO3融相图.
结论:
- 深度学习潜力模型准确地模拟了高压和高温下CaO3的行为.
- 这项研究为了解下层地幔的组成和动态提供了关键数据.
- 结果有助于行星进化和内部过程的模型.
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