从元动力学的coesite密集的动态路径
1Department of Experimental Physics, Faculty of Mathematics, Physics and Informatics, Comenius University, Mlynská Dolina F2, 84248 Bratislava, Slovakia.
The Journal of chemical physics
|September 19, 2025
概括
高压将coesite转化为新的晶体相,如coesite-IV和八面体结构. 使用机器学习潜力的计算方法成功模拟了这些复杂的结构转换及其原子化机制.
科学领域:
- 矿物物理 矿物物理
- 计算材料科学科学 计算材料科学
- 地质化学 地质化学
背景情况:
- 石是SiO2的高压多态,在极端压力下经历了显著的结构变化.
- 之前的实验研究确定了转移稳定的coesite-II和coesite-III,其次是重建转化为八面体相,coesite-IV和coesite-V的30GPa以上.
- 通过计算来模拟这些复杂的重建转换,由于大规模的结构重组和协调变化,这是一项重大挑战.
研究的目的:
- 通过计算来研究coesite的高压压缩,并阐明其结构转换的原子化机制.
- 验证一种结合元动力学,机器学习潜力和集体变量的计算方法,用于研究复杂的相位过渡.
- 识别和描述导致观察到实验结果的不同转换途径.
主要方法:
- 使用Si-O协调数和体积作为集体变量的元动力学模拟.
- 采用基于先进的机器学习的原子潜力 (ACE潜力) 进行精确的能量和力计算.
- 分析了转化途径的原子化机制,包括无形化,coesite-IV的形成和八面体相.
主要成果:
- 计算方法成功地重现了所有实验观察到的转换路径:无形化,coesite-IV和八面体相.
- 详细描述了转化为coesite-IV的详细原子化机制以及八面体相的两步过程.
- 预计在室温下,coesite-IV的途径更容易形成,而在600 K时,八面体相位形成更有可能发生.
结论:
- 机器学习潜力与元动力学相结合,为研究矿物中复杂的重建相过渡提供了强大的工具.
- 该研究澄清了coesite压缩的原子细节,提供了对地球深层内部和极端条件下的材料的见解.
- 预测了取决于温度的路径选择,突出了热能对高压矿物转化的影响.
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