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Long Time Scale Molecular Dynamics Simulation of Magnesium Hydride Dehydrogenation Enabled by Machine Learning
Oliver Morrison1, Elena Uteva1, Gavin S Walker2
1Advanced Materials Research Group, Faculty of Engineering, University of Nottingham, Nottingham NG7 2RD, United Kingdom.
ACS Applied Energy Materials
|January 17, 2025
Summary
Machine learning simulations reveal that subsurface molecular hydrogen (H2) formation and trapping in magnesium hydride (MgH2) hinders its dehydrogenation kinetics. Improving H2 diffusion or blocking subsurface formation can enhance hydrogen storage performance.
Area of Science:
- Materials Science
- Computational Chemistry
- Chemical Engineering
Background:
- Magnesium hydride (MgH2) is a cost-effective material for hydrogen storage.
- Its practical application is limited by high dehydrogenation temperatures and slow kinetics.
- Catalysts and physical restructuring improve kinetics, but mechanisms remain unclear.
Purpose of the Study:
- To investigate the underlying mechanisms of MgH2 dehydrogenation kinetics.
- To explore the role of subsurface hydrogen behavior.
- To develop a predictive model for MgH2 material properties.
Main Methods:
- Development of a machine learning interatomic potential (MLP) for the Mg-H system.
- Long time-scale molecular dynamics (MD) simulations (up to 1 ns) of MgH2 surface slabs.
- Analysis of molecular hydrogen (H2) formation and diffusion dynamics.
Main Results:
- Unprecedented observation of subsurface H2 formation within MgH2.
- Identification of subsurface H2 trapping as a key factor in slow dehydrogenation.
- Correlation between subsurface H2 diffusion and overall reaction kinetics.
Conclusions:
- Subsurface H2 behavior significantly impacts MgH2 dehydrogenation kinetics.
- Catalysts may improve kinetics by inhibiting subsurface H2 formation and trapping.
- Physical restructuring could enhance kinetics by facilitating subsurface H2 diffusion via defects.
