A new insight of structures, bonding and electronic properties for 6-mercaptopurine and Ag8 clusters configurations: a theoretical perspective

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.

Adsorption/Desorption Behaviors and SERS Chemical Enhancement of 6-Mercaptopurine on a Nanostructured Gold Surface: The Au20 Cluster Model

Computational approaches are employed to elucidate the binding mechanism and the SERS phenomenon of 6-mercaptopurine (6MP) adsorbed on the tetrahedral Au20 cluster as a simple model for a nanostructured gold surface. Computations are carried out in both vacuum and aqueous environments using a continuum model. In the gaseous phase and neutral conditions, interaction of 6MP with the gold cluster is mostly dominated by a covalent Au-S bond and partially stabilized by the Au⋅⋅⋅H-N coupling. However, in acidic solution, the nonconventional Au⋅⋅⋅H-S hydrogen-bond becomes the most favorable binding mode. The 6MP affinity for gold clusters decreases in the order of vacuum > neutral solution > acidic medium. During the adsorption, the energy gap of Au20 substantially declines, leading to an increase in its electrical conductivity, which can be converted to an electrical noise. Moreover, such interaction is likely a reversible process and triggered by either the low pH in sick tissues or the presence of cysteine residues in protein matrices. While N-H bending and stretching vibrations play major roles in the SERS phenomenon of 6MP on gold surfaces in neutral solution, the strongest enhancement in acidic environment is mostly due to an Au⋅⋅⋅H-S coupling, rather than an aromatic ring-gold surface π overlap as previously proposed.