Variable Lattice Monte Carlo: A Voronoi Tessellation-Guided Structural Exploration Framework and Applications in Iron...

Area of Science:

• Computational materials science
• Catalysis
• Surface science

Background:

• Identifying ground-state structures and metastable phases of heterogeneous catalysts with evolving active phases, like iron carbides in Fischer-Tropsch synthesis (FTS), is challenging.
• Existing theoretical methods struggle with the dynamic nature of active sites and complex potential energy surfaces.

Purpose of the Study:

• To introduce a novel computational framework, the Variable Lattice Monte Carlo (VLMC) method, for global optimization and statistical thermodynamic sampling.
• To enable efficient exploration of chemical space for systems with variable metal lattices and fractional interstitial site occupation.

Main Methods:

• Developed the Variable Lattice Monte Carlo (VLMC) method, integrating Voronoi tessellation for dynamic targeting of interstitial and adsorption sites.
• Applied VLMC to three representative cases: phase transformations in iron carbides (χ-Fe5C2, θ-Fe3C to η-Fe2C), carbon-induced surface reconstruction on Fe(100), and nanoparticle morphology evolution.

Main Results:

• VLMC successfully navigated complex potential energy surfaces to identify ground-state structures and metastable phases of iron carbides.
• Demonstrated the method's capability in simulating phase transformations, surface reconstructions, and nanoparticle morphology evolution under varying conditions.

Conclusions:

• The Variable Lattice Monte Carlo (VLMC) method provides a robust framework for structural exploration in iron carbides and similar catalytic systems.
• VLMC facilitates efficient discovery of catalyst structures and phases crucial for optimizing reactions like Fischer-Tropsch synthesis.