Molecular dynamics simulation of nitrogen diffusion in iron and iron nitrides using ab initio data trained machine...

Area of Science:

• Materials Science
• Computational Chemistry
• Chemical Engineering

Background:

• Ammonia is a key green fuel for carbon neutrality, but its use in combustors causes surface nitriding of metal walls.
• Accurate prediction of nitrogen diffusion in iron and iron nitrides is crucial for modeling this nitridation process.
• Experimental determination of diffusion coefficients is difficult due to steep gradients and phase transformations.

Purpose of the Study:

• To calculate temperature- and concentration-dependent nitrogen atom diffusion coefficients in iron and iron nitrides using molecular dynamics (MD) driven by a machine learning interatomic potential (MLP).
• To enable precise modeling of nitrogen diffusion and "unwanted" nitriding in ammonia-fueled combustors.

Main Methods:

• Developed an MLP trained on ab initio molecular dynamics (AIMD) data covering various temperatures and nitrogen concentrations.
• Employed MLP-driven MD simulations to compute nitrogen diffusion coefficients in different iron and iron nitride phases (α-, γ-iron, γ'-, ε-iron nitride).
• Converted self-diffusion coefficients to chemical (Fickian) diffusion coefficients for comparison with experimental data.

Main Results:

• The MLP accurately reproduced DFT-calculated diffusion energy barriers for iron and iron nitrides.
• MLP-driven MD simulations yielded diffusion coefficients approaching ab initio accuracy.
• Calculated chemical diffusion coefficients at 830-1500 K matched experimental activation energy and pre-exponential factors, with extrapolation to 823 K falling within experimental uncertainty.

Conclusions:

• The developed MLP-MD model provides accurate, concentration- and temperature-resolved nitrogen diffusion coefficients in iron and its nitrides.
• This model facilitates mechanistic understanding and quantitative prediction of "unwanted" nitriding in iron-based combustor materials.
• The findings support the safe and efficient utilization of ammonia as a green fuel.