相关实验视频
Updated: Jan 6, 2026

06:53
Fabrication and Optimization of Type II Silicon Clathrate Films
Published on: October 14, 2025
941
深度学习驱动的预测策略,用于化XCl (X = Li,Na,或K) 的相变行为
Heqing Tian1, Tianyu Liu1, Xianyou Lan1
1School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001, China.
Langmuir : the ACS journal of surfaces and colloids
|October 2, 2025
概括
这项研究使用深度潜在的分子动力学来分析化盐相位过渡. 模拟显示相位过渡发生在超热/超冷点附近,而不是点附近,并观察到歇斯底里.
科学领域:
- 材料科学 材料科学 材料科学
- 物理化学 物理化学
- 计算化学的计算化学
背景情况:
- 金属化物在各种工业应用中至关重要,需要准确了解它们的热性能.
- 盐的相变行为是复杂的,使用传统方法难以准确预测.
研究的目的:
- 系统地研究性金属化物盐的相变特性.
- 建立一个可靠的计算框架,以高精度预测盐的热性能.
主要方法:
- 在模拟中使用了深潜分子动力学 (DPMD) 策略.
- 使用超热-超冷歇斯底里法来确定点.
- 分析包括辐射分布函数 (RDF),平均平方位移 (MSD),自我扩散系数 (D),协调数 (CN) 和分子动力学轨迹.
主要成果:
- 模拟捕获了盐化过程中局部阴离子和阴离子环境的突然变化.
- 融盐表现出近超热的长距离有序过渡和近超冷的长距离无序过渡.
- 在MSD,D和CN分析中,在热循环和离子协调结构重组过程中定量证明了hysteresis.
结论:
- 化的盐在超热和超冷温度附近经历相变,而不是在点.
- 这项研究揭示了融盐中的固体-液体相变动力学.
- 建立了一个可靠的计算框架,用于准确预测融盐的热性能.
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