Molecular Dynamics Study of Hydrogen Release from NaH Using Machine Learning Potential

Sodium hydride (NaH) is the main by-product generated during the operation of the cold traps in a sodium-cooled fast reactor. Its thermal decomposition releases hydrogen gas, posing a safety hazard. However, understanding the atomic-scale decomposition mechanism remains challenging because conventional simulation methods are limited in the accessible length and time scales. This study developed a deep neural network potential (DP) for the NaH system using the DP-GEN active learning framework. Benchmark tests show that the DP model accurately reproduces density functional theory (DFT) reference energies, forces, equations of state, elastic properties, and phonon spectra. In particular, the DP-predicted bulk modulus and lattice constant are in good agreement with DFT results and close to experimental values, significantly outperforming the empirical ReaxFF potential. Subsequently, we conducted large-scale Deep Potential Molecular Dynamics (DPMD) simulations to investigate the thermal decomposition behavior of NaH clusters and a slab model. The simulation results reveal model-dependent thermal responses of NaH. In the original Na₄₈H₄₈ cluster simulation heated from 100 to 1200 K, a structural transition and disordering were observed, but no H₂ formation occurred within the simulation time. In contrast, the slab model heated from 300 to 1500 K exhibited surface disordering, Na-H bond cleavage, H-H bond formation, cluster detachment, and H₂ formation and release at elevated temperatures. A supplementary higher-temperature Na₄₈H₄₈ cluster simulation further showed cluster dissociation in the 1000-1500 K range. These results provide atomistic insight into the model- and temperature-dependent decomposition behavior of NaH and suggest that the DP model is a useful tool for studying hydrogen-related processes in alkali metal hydrides.