Global minimization of gold clusters by combining neural network potentials and the basin-hopping method

Abstract

Neural network potentials trained by first-principles density functional theory total energies were applied to search for global minima of gold nanoclusters within the basin-hopping method. Using Au₅₈ as an example, we found a new putative global minimum which has a core–shell structure of Au₁₀@Au₄₈ and C₄ symmetry. This new structure of Au₅₈ is 0.24 eV per formula more stable than the best previous model that has C₁ symmetry. This work demonstrates that neural network potentials combined with the basin-hopping method could be very useful in global minimization for medium-sized metal clusters which might be computationally prohibitive for first principles density functional theory.