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Main Authors: Sato, Ryuhei, Cooke, Peter I. C., Caussé, Maélie, Tran, Hung Ba, Jang, Seong Hoon, Zhang, Di, Li, Hao, Orimo, Shin-ichi, Shibuta, Yasushi, Pickard, Chris J.
Format: Preprint
Published: 2026
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Online Access:https://arxiv.org/abs/2603.08950
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author Sato, Ryuhei
Cooke, Peter I. C.
Caussé, Maélie
Tran, Hung Ba
Jang, Seong Hoon
Zhang, Di
Li, Hao
Orimo, Shin-ichi
Shibuta, Yasushi
Pickard, Chris J.
author_facet Sato, Ryuhei
Cooke, Peter I. C.
Caussé, Maélie
Tran, Hung Ba
Jang, Seong Hoon
Zhang, Di
Li, Hao
Orimo, Shin-ichi
Shibuta, Yasushi
Pickard, Chris J.
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2603_08950
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Competing Hydrogenation Pathways to Metastable CaH$_6$ Revealed by Machine-Learning-Potential Molecular Dynamics
Sato, Ryuhei
Cooke, Peter I. C.
Caussé, Maélie
Tran, Hung Ba
Jang, Seong Hoon
Zhang, Di
Li, Hao
Orimo, Shin-ichi
Shibuta, Yasushi
Pickard, Chris J.
Materials Science
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.
title Competing Hydrogenation Pathways to Metastable CaH$_6$ Revealed by Machine-Learning-Potential Molecular Dynamics
topic Materials Science
url https://arxiv.org/abs/2603.08950