Long Time Scale Molecular Dynamics Simulation of Magnesium Hydride Dehydrogenation Enabled by Machine Learning Interatomic Potentials
(2024)
Journal Article
Morrison, O., Uteva, E., Walker, G. S., Grant, D. M., & Ling, S. (in press). Long Time Scale Molecular Dynamics Simulation of Magnesium Hydride Dehydrogenation Enabled by Machine Learning Interatomic Potentials. ACS Applied Energy Materials, https://doi.org/10.1021/acsaem.4c02627
Magnesium hydride (MgH2) is a promising material for solid-state hydrogen storage due to its high gravimetric hydrogen capacity as well as the abundance and low cost of magnesium. The material’s limiting factor is the high dehydrogenation temperature... Read More about Long Time Scale Molecular Dynamics Simulation of Magnesium Hydride Dehydrogenation Enabled by Machine Learning Interatomic Potentials.