An Efficient Growth Pattern Algorithm (GrowPAL) for Cluster Structure Prediction.

Abstract

Identifying the lowest energy isomers in large clusters is a major challenge. Here, we introduce the Growth Pattern Algorithm (GrowPAL), a new approach that generates initial seeds composed of n+1 atoms from the system with n atoms through an interstitial-type addition (I-type) mechanism. We evaluated the effectiveness of GrowPAL on Lennard-Jones (LJ) clusters with up to n = 80 atoms, verifying the algorithm's ability to find challenging minima such as LJ38 and the partially icosahedral LJ69 with fewer optimizations than existing methods. In addition, we discuss the advantages and limitations of GrowPAL using our deconstruction scheme, which identifies "forebears" structures to study growth pathways. Having evaluated the strengths and weaknesses of GrowPAL, we employed it to explore Sutton-Chen clusters containing 5 to 80 atoms, uncovering three new lowest energy forms. We then applied GrowPAL to boron clusters containing 8 to 15 atoms, successfully identifying all reported minima. Overall, GrowPAL offers a practical solution for efficiently identifying global minima in hierarchical systems, thereby reducing computational costs.