Pool-BCGA: a parallelised generation-free genetic algorithm for the ab initio global optimisation of nanoalloy...

Area of Science:

• Computational chemistry
• Materials science
• Algorithm development

Background:

• Global optimization of atomic clusters is crucial for understanding material properties.
• Existing algorithms can be computationally intensive and slow.

Purpose of the Study:

• To present a novel parallel implementation of the Birmingham cluster genetic algorithm.
• To improve the performance and efficiency of global optimization for atomic clusters.

Main Methods:

• Implementation of a parallel genetic algorithm with a pool strategy.
• Testing with the Gupta potential for Au10Pd10 cluster optimization.
• Application to Density Functional Theory (DFT) level optimization of Au10 and Au20 clusters.

Main Results:

• The new parallel implementation significantly improves computational performance.
• Demonstrated high efficiency for global optimization of bimetallic (Au10Pd10) and monometallic (Au10, Au20) clusters.
• Successful application of the algorithm at the DFT level.

Conclusions:

• The parallel pool genetic algorithm is an efficient and effective tool for atomic cluster global optimization.
• This advancement accelerates simulations in computational chemistry and materials science.
• The method is applicable to both empirical potential and first-principles calculations.