Cluster structure prediction via CALYPSO method*

Abstract

Cluster science as a bridge linking atomic molecular physics and condensed matter inspired the nanomaterials development in the past decades, ranging from the single-atom catalysis to ligand-protected noble metal clusters. The corresponding studies not only have been restricted to the search for the geometrical structures of clusters, but also have promoted the development of cluster-assembled materials as the building blocks. The CALYPSO cluster prediction method combined with other computational techniques have significantly stimulated the development of the cluster-based nanomaterials. In this review, we will summarize some good cases of cluster structure by CALYPSO method, which have also been successfully identified by the photoelectron spectra experiments. Beginning with the alkali-metal clusters, which serve as benchmarks, a series of studies are performed on the size-dependent elemental clusters which possess relatively high stability and interesting chemical physical properties. Special attentions are paid to the boron-based clusters because of their promising applications. The NbSi12 and BeB16 clusters, for example, are two classic representatives of the silicon- and boron-based clusters, which can be viewed as building blocks of nanotubes and borophene. This review offers a detailed description of the structural evolutions and electronic properties of medium-sized pure and doped clusters, which will advance fundamental knowledge of cluster-based nanomaterials and provide valuable information for further theoretical and experimental studies.

Machine learning in materials design: Algorithm and application*

“…The crystal structure analysis by particle swarm optimization (CALYPSO) developed by Wang et al, [173,174] was built on another classic search algorithm called particle swarm optimization (PSO). CALYPSO had been successfully applied in the design of various materials structures such as 3D crystals, [174] clusters, [175] and 2D materials. [176] Other algorithms that have been applied in materials structures search includes the ab initio random structure searching (AIRSS), [177] simulated annealing, [178][179][180] basin hopping, [181][182][183] and minima hopping.…”

MeGen - generation of gallium metal clusters using reinforcement learning

“…For example, random search provides an unbiased configuration sampling [7] while genetic algorithm combines and propagates useful structural markers [8,9]. Basin-hopping [10,11] represents an efficient technique for escaping from local minima and mapping the potential energy surface (PES) and particle swarm optimization [12,13] relies on the population information for navigating the energy landscape. However, when these techniques are combined with ab-initio methods for the description of inter-atomic interaction, they become computationally expensive and result in an insufficient sampling of PES [14,15].…”

Structural evolution, charge transfer and bonding properties of medium-sized atomic rubidium-doped boron clusters

“…With so many interesting phenomena associated with tm-doped B clusters, it is interesting to see what happens when B clusters are doped with Rb atoms, which are alkali metals. 38,39…”

“…35 In addition, there are many medium-sized doped boron clusters with interesting structural evolution. 36,37 In the CALYPSO based cluster structure prediction study of Tian's group, we found that they mentioned a number of interesting phenomena for metal-doped boron clusters with Li 2 B 24 tubular structures, MgB 18 drum-like tubular clusters, BeB 38,39 The properties and geometry of atomic clusters are sizedependent and highly sensitive to the nature of the dopant. [40][41][42] Yang's group searched for the structure of the Cs 2 B n (n = 1-12) clusters using unbiased crystal structure analysis and determined the global minimum.…”