对CeO2稳定ZrO2的温度依赖格子热性能的洞察力,由机器学习力场稳定ZrO2
Linpo Yang1, Jia Wei2, Yinglin Song3,4
1College of Mathematics and Physics, Nanyang Institute of Technology, Nanyang, 473004, Henan, China.
Physical chemistry chemical physics : PCCP
|January 13, 2026
概括
这项研究开发了ZrO2-CeO2系统的机器学习原子间潜力,使热导率和相变的准确模拟成为可能. 研究结果显示,室温下的导热率很低,这对于热保护应用至关重要.
科学领域:
- 材料科学 材料科学 材料科学
- 计算物理 计算物理
- 热力学是一种热力学.
背景情况:
- 在ZrO2-CeO2中,格子导热率 (LTC) 对于热保护应用至关重要.
- 了解控制LTC和相位转换的原子规模过程是具有挑战性的.
- 像密度函数理论这样的现有方法在尺寸和温度效应方面存在局限性.
研究的目的:
- 为ZrO2-CeO2系统开发一个机器学习 (ML) 原子间潜力.
- 在大型超级晶格系统中研究热力学过程,包括相变和LTC.
- 克服模拟热性质的传统方法的局限性.
主要方法:
- 最远点采样 (FPS) 用于数据集采样.
- 机器学习 (ML) 方法来导出原子间潜力.
- 在分子动力学 (MD) 模拟中,神经进化潜力 (NEP) 框架.
- 同质非平衡分子动力学 (HNEMD) 用于导热分析.
- 用于机制分析的光谱分解.
主要成果:
- 成功得出了一个ML原子间潜力,克服了DFT的局限性.
- 在加热过程中观察到显著的第一阶段和第二阶段相位过渡.
- 由于格子尺寸效应,研究了导热率变化.
- 使用NEP-MD.在不同的兴奋剂度下预测的晶格导热率.
- 系统在室温下表现出相对较低的导热率.
结论:
- 开发的ML潜力准确地模拟了ZrO2-CeO2.2.中的热特性和相变.
- 该研究提供了对这些材料热传输机制的见解.
- 这些发现对于优化用于热保护和其他工业应用的ZrO2-CeO2材料至关重要.
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