Competing Hydrogenation Pathways to Metastable CaH${}_6$ Revealed by Machine-Learning-Potential Molecular Dynamics

Abstract

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.

5 figures with supporting information