AI-Driven Coverage-Dependent Kinetics for NH₃ Synthesis on Fe(110).

Area of Science:

• Heterogeneous catalysis
• Computational chemistry
• Materials science

Background:

• Adsorbate-adsorbate interactions link mass transfer and kinetics but are underexplored.
• Dynamic characterization of these interactions is crucial for understanding catalytic mechanisms.

Purpose of the Study:

• To establish a quantitative framework for ammonia synthesis over Fe(110) by integrating DFT, AI, and kinetic modeling.
• To investigate the dynamic role of adsorbate-adsorbate interactions and surface coverage in catalytic performance.

Main Methods:

• Density Functional Theory (DFT) for electronic structure calculations.
• Artificial intelligence (AI)-driven structural screening with NequIP for identifying low-energy adsorption configurations.
• Coverage-dependent kinetic modeling to predict reaction rates and identify rate-determining steps.

Main Results:

• AI screening achieved high-precision energy prediction (MAE = 0.028 eV).
• A coverage-dependent model predicted a turnover frequency (TOF) of 4.4 × 10^-7 s^-1 at 673.15 K and 300 mbar.
• Atomic hydrogen was found to dominate the surface (69.4%) due to repulsive interactions, and rate-determining steps were identified based on temperature.

Conclusions:

• Coverage and temperature critically regulate rate-determining steps in ammonia synthesis.
• This work provides a paradigm for connecting macroscopic conditions to microscopic surface dynamics for catalyst design, especially for low-pressure ammonia synthesis.