Nanoclusters and nanoalloys of group 13 elements (B, Al, and Ga): benchmarking of methods and analysis of their structures and energies

A reinvestigation of the boron cluster B15+/0/-: a benchmark of density functionals and consideration of aromaticity models

This study presents a thorough reinvestigation of the B15+/0/- isomers, first employing coupled-cluster theory CCSD(T) calculations to validate the performance of different DFT functionals. The B15+ cation has two planar lowest-lying isomers, while the first 3D isomer is less stable than the global minimum by ∼10 kcal mol-1. The PBE functional, within this benchmark survey, has proved to be reliable in predicting relative energies for boron isomers. Other functionals such as the TPSSh, PBE0 and HSE06 result in good energy ordering of isomers but warrant reconsideration when distinguishing between 2D and 3D forms. Caution is needed for structures having high spin contamination, as it may lead to significant errors. The anomalously lower stability of the B15- anion with respect to its neighbours, in terms of electron detachment energy, was explained through a competition between both rectangle and disk models for its geometry. This elucidates its stability with 12 electrons in rectangle model and instability with 10 electrons in disk-shaped structure, emphasizing the value of employing such geometric models. The proximity of the σ* LUMO to the π HOMO also contributes to the weakening of the B15- stability.

Cluster energy prediction based on multiple strategy fusion whale optimization algorithm and light gradient boosting machine

BACKGROUND

Clusters, a novel hierarchical material structure that emerges from atoms or molecules, possess unique reactivity and catalytic properties, crucial in catalysis, biomedicine, and optoelectronics. Predicting cluster energy provides insights into electronic structure, magnetism, and stability. However, the structure of clusters and their potential energy surface is exceptionally intricate. Searching for the global optimal structure (the lowest energy) among these isomers poses a significant challenge. Currently, modelling cluster energy predictions with traditional machine learning methods has several issues, including reliance on manual expertise, slow computation, heavy computational resource demands, and less efficient parameter tuning.

RESULTS

This paper introduces a predictive model for the energy of a gold cluster comprising twenty atoms (referred to as Au20 cluster). The model integrates the Multiple Strategy Fusion Whale Optimization Algorithm (MSFWOA) with the Light Gradient Boosting Machine (LightGBM), resulting in the MSFWOA-LightGBM model. This model employs the Coulomb matrix representation and eigenvalue solution methods for feature extraction. Additionally, it incorporates the Tent chaotic mapping, cosine convergence factor, and inertia weight updating strategy to optimize the Whale Optimization Algorithm (WOA), leading to the development of MSFWOA. Subsequently, MSFWOA is employed to optimize the parameters of LightGBM for supporting the energy prediction of Au20 cluster.

CONCLUSIONS

The experimental results show that the most stable Au20 cluster structure is a regular tetrahedron with the lowest energy, displaying tight and uniform atom distribution, high geometric symmetry. Compared to other models, the MSFWOA-LightGBM model excels in accuracy and correlation, with MSE, RMSE, and R2 values of 0.897, 0.947, and 0.879, respectively. Additionally, the MSFWOA-LightGBM model possesses outstanding scalability, offering valuable insights for material design, energy storage, sensing technology, and biomedical imaging, with the potential to drive research and development in these areas.

Underlying mechanisms of gold nanoalloys stabilization

Gold nanoclusters have attracted significant attention due to their unique physical-chemical properties, which can be tuned by alloying with elements such as Cu, Pd, Ag, and Pt to design materials for various applications. Although Au-nanoalloys have promising applications, our atomistic understanding of the descriptors that drive their stability is far from satisfactory. To address this problem, we considered 55-atom model nanoalloys that have been synthesized by experimental techniques. Here, we combined data mining techniques for creating a large sample of representative configurations, density functional theory for performing total energy optimizations, and Spearman correlation analyses to identify the most important descriptors. Among our results, we have identified trends in core-shell formation in the AuCu and AuPd systems and an onion-like design in the AuAg system, characterized by the aggregation of gold atoms on nanocluster surfaces. These features are explained by Au's surface energy, packing efficiency, and charge transfer mechanisms, which are enhanced by the alloys' preference for adopting the structure of the alloying metal rather than the low-symmetry one presented by Au55. These generalizations provide insights into the interplay between electronic and structural properties in gold nanoalloys, contributing to the understanding of their stabilization mechanisms and potential applications in various fields.