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Competing hydrogenation pathways to metastable CaH6 revealed by machine learning potential molecular dynamics

Ryuhei Sato1,*, Peter I. C. Cooke2,†, Maélie Caussé2,3, Hung Ba Tran3, Seong Hoon Jang3, Di Zhang3, Hao Li3, Shin-ichi Orimo3,4, Yasushi Shibuta1 et al.

Chris J. Pickard2,3

  • *Contact author: r-sato@g.ecc.u-tokyo.ac.jp
  • †Contact author: pc729@cam.ac.uk
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Phys. Rev. B 114, 074102 – Published 5 August, 2026

DOI: https://doi.org/10.1103/pb7t-5bj1

Abstract

The synthesis of the high-Tc superhydride CaH6 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 simulations can reproduce and distinguish competing reaction pathways leading to metastable and stable hydrides. By simulating hydrogenation reactions at CaH2/H2 and CaH4/H2 interfaces, we identify two distinct pathways that produce clathrate-type CaH6 and A15-type CaH5.75, respectively. CaH5.75 lies on the convex hull but requires extensive Ca sublattice rearrangement and therefore forms only at elevated temperatures. In contrast, CaH6 becomes kinetically accessible when CaH2 is used as the precursor. The crystallographic compatibility between the Ca sublattice of CaH2 and the body-centered cubic framework of CaH6 enables a martensitic-like topotactic transformation that bypasses the reconstructive pathway leading to CaH5.75. 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.

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Phys. Rev. B 114, 074102– Published 5 August, 2026
  • Received 9 March 2026
  • Revised 24 June 2026
  • Accepted 15 July 2026
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