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Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
Published on: April 8, 2020
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Unbiased fuzzy global optimization of Lennard-Jones clusters for N ≤ 1000
Kailiang Yu1, Xubo Wang1, Liping Chen2
1Center for Chemistry of Novel & High-Performance Materials, Department of Chemistry, Zhejiang University, Hangzhou 310027, China.
The Journal of Chemical Physics
|December 12, 2019
Summary
We developed a fuzzy global optimization (FGO) algorithm for finding the lowest-energy nanocluster structures. This efficient method successfully identifies global minima for large Lennard-Jones clusters, discovering new structures.
Area of Science:
- Computational chemistry
- Materials science
- Nanotechnology
Background:
- Determining the lowest-energy structure of nanoclusters is crucial for understanding their properties.
- Traditional real-space optimization methods can be computationally expensive and may not find the global minimum.
Purpose of the Study:
- To introduce a novel fuzzy global optimization (FGO) algorithm for efficient and reliable identification of nanocluster global minimum structures.
- To demonstrate the effectiveness of FGO on large Lennard-Jones (LJ) clusters.
Main Methods:
- FGO utilizes a fuzzy search framework, primarily in discrete space, combined with Monte Carlo simulations (directed and surface).
- Low-energy candidate structures are refined using real-space local optimizations to pinpoint the global minimum.
- The algorithm was tested on a comprehensive set of LJ clusters up to 1000 atoms.
Main Results:
- FGO successfully identified all known global minima for the tested LJ clusters.
- The algorithm exhibits low computational time scaling with increasing cluster size.
- New global minimum structures were discovered for LJ clusters containing 894, 974, and 991 atoms.
Conclusions:
- FGO is a highly efficient and reliable method for global optimization of nanocluster structures.
- Its unbiased nature makes it applicable to a wide range of nanomaterials.
- The algorithm offers a significant advancement in computational materials science for structure prediction.
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