A Coordination-Based Model for the Prediction of Surface Energies and the Shape of Metal Particles

TL;DR

This paper introduces a coordination number-based model to predict surface energies and nanoparticle shapes of metals efficiently, bypassing complex DFT calculations and enabling rapid on-the-fly analysis.

Contribution

The authors developed a novel coordination-based model that accurately predicts metal surface energies and shapes, improving upon existing methods by accounting for atomic layer differences.

Findings

Model closely matches DFT calculations for late transition metals.

Enables rapid surface energy predictions without DFT.

Provides insights into nanoparticle surface stability.

Abstract

Surface energies of metal-based systems are important for determining the Wulff-constructed shapes of metal nanoparticles and understanding the stability. We have developed a coordination number-based model to predict the total energy of metal-based systems across a wide range of configurations. Our model has been tested against Density Functional Theory (DFT) calculations for late transition metals. This method enables on-the-fly surface energy predictions and allows for the Wulff construction of metal particles for a random number of elemental atoms and without the need for DFT calculations. By making a division between atoms in the different layers of the model system we can considerably improve the accuracy of the model, suggesting a dissimilarity between the electronic structure due to an alternating compression and expansion of atomic layers in the near-surface region. We find that our model accurately and effectively provides valuable insights into the distribution and stability of nanoparticle surfaces.