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

TL;DR

This study uses machine-learning potential molecular dynamics to elucidate competing hydrogenation pathways in calcium hydrides, revealing how precursor structures influence the formation of metastable superhydrides like CaH$_6$.

Contribution

The paper demonstrates that machine-learning molecular dynamics can distinguish and simulate different hydrogenation pathways leading to metastable and stable calcium hydrides.

Findings

CaH$_6$ formation is kinetically favored from CaH$_2$ precursors.

Two distinct pathways produce CaH$_6$ and CaH$_{5.75}$, with different temperature dependencies.

Crystallographic compatibility enables a topotactic transformation to CaH$_6$.

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.