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 stabili...

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Veröffentlicht in:BMC chemistry Jg. 18; H. 1; S. 24 - 18
Hauptverfasser: Wei, Wu, Mengshan, Li, Yan, Wu, Lixin, Guan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cham Springer International Publishing 30.01.2024
BioMed Central Ltd
Springer Nature B.V
BMC
Schlagworte:
Algorithms
Catalysis
Chemistry
Chemistry and Materials Science
Chemistry/Food Science
Cluster
Clusters
Eigenvalues
Electronic structure
Energy
Energy prediction
Energy storage
Feature extraction
Force and energy
LightGBM
Machine Learning
Magnetism
Marine mammals
Mathematical models
Mathematical optimization
Matrix representation
Medical imaging
Model accuracy
Molecular structure
Optimization
Optimization algorithms
Optoelectronics
Parameters
Potential energy
Prediction models
R&D
Research & development
Tetrahedra
Energy prediction
Cluster
Machine Learning
LightGBM
ISSN:2661-801X, 2661-801X
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Zusammenfassung: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 R 2 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. Graphical Abstract
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ISSN:2661-801X
2661-801X
DOI:10.1186/s13065-024-01127-0