2025/06/18 by Shyama Charan Mandal, Frank Abild‐Pedersen, Mandal, Shyama Charan +1
Chemical Engineering · Materials Science · #Catalysis and Oxidation Reactions #FOS: Physical sciences #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci) #X-ray Diffraction in Crystallography
paper · pdf · doi:10.48550/arxiv.2506.15144
openalex publication_date 2025/06/18 · openalex created_date 2025/10/19 · openalex updated_date 2026/08/01
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.