When size determines crystal structure: crossover between cubic and hexagonal structures of molybdenum carbide...

Area of Science:

• Materials Science
• Computational Chemistry
• Nanotechnology

Background:

• Molybdenum carbide (MoC) nanoparticles (NPs) show promise for catalysis.
• Studying large-scale MoC NP models with first-principles methods is computationally prohibitive.

Purpose of the Study:

• To develop an efficient workflow for predicting properties of large-scale MoC NPs.
• To investigate the size-dependent morphology and stability of MoC NPs.

Main Methods:

• An on-the-fly machine learning force field (MLFF) workflow was implemented.
• Density Functional Theory (DFT) calculations were used to sample various MoC structures (bulks, slabs, clusters).

Main Results:

• Subnanometric cubic δ-MoC clusters are stable; metastable hexagonal α-MoC clusters are structurally flexible.
• A phase transition crossover diameter of ~4.3 nm was identified.
• Above this diameter, α-MoC NPs become the most stable morphology, explaining experimental observations of phase prevalence at different sizes.

Conclusions:

• The MLFF workflow enables reliable prediction of realistic MoC NP properties.
• Insights into size-dependent morphology are provided, aiding the rational design of MoC-based nanocatalysts.