揭示稳定CaH₆形成路径的竞争氢化途径
Competing Hydrogenation Pathways to Metastable CaH$_6$ Revealed by Machine-Learning-Potential Molecular Dynamics
AI总结：
通过机器学习分子动力学研究揭示了稳定CaH₆形成路径的竞争氢化机制
摘要：
高临界温度超氢化物CaH₆的合成激发了对稳定氢化物合成路径的广泛关注。然而，此类氢化反应的微观机制仍不明确。本文表明，机器学习势分子动力学（MLP-MD）模拟能够重现并区分导致稳定和不稳定的氢化物的竞争反应路径。通过在CaH₂/H₂和CaH₄/H₂界面模拟氢化反应，我们识别出两种不同的路径，分别产生笼型CaH₆和A15型CaH₅.₇₅。CaH₅.₇₅位于凸包上，但需要大量的Ca子晶格重构，因此只能在高温下形成。相比之下，当使用CaH₂作为前驱体时，CaH₆在动力学上是可接触的。CaH₂的Ca子晶格与CaH₆的bcc框架的晶格兼容性，使了一种类似马氏体的同质异相转变，绕过了导致CaH₅.₇₅的重构路径。这些结果揭示了前驱体结构和热力学稳定性如何竞争决定超氢化物的形成路径，并展示了机器学习分子动力学能够直接捕捉反应材料系统中稳定相的动能选择。
英文摘要：
The synthesis of the high-$T_c$ superhydride CaH$_6$ has stimulated significant interest in understanding synthesis pathways for metastable hydrides. However, the microscopic mechanisms governing such hydrogenation reactions remain poorly understood. Here, we show that machine-learning potential molecular dynamics (MLP-MD) simulations can reproduce and distinguish competing reaction pathways leading to metastable and stable hydrides. By simulating hydrogenation reactions at CaH$_2$/H$_2$ and CaH$_4$/H$_2$ interfaces, we identify two distinct pathways that produce clathrate-type CaH$_6$ and A15-type CaH$_{5.75}$, respectively. CaH$_{5.75}$ lies on the convex hull but requires extensive Ca sublattice rearrangement and therefore forms only at elevated temperatures. In contrast, CaH$_6$ becomes kinetically accessible when CaH$_2$ is used as the precursor. The crystallographic compatibility between the Ca sublattice of CaH$_2$ and the bcc framework of CaH$_6$ enables a martensitic-like topotactic transformation that bypasses the reconstructive pathway leading to CaH$_{5.75}$. These results reveal how precursor structure and thermodynamic stability compete to determine superhydride formation pathways and demonstrate that machine-learning molecular dynamics can directly capture the kinetic selection of metastable phases in reactive materials systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
