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C++ Toolkit for Bimetallic Cluster Structure Optimization Using Collaborative Differential Evolution
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DOI：10.1021/acs.jcim.5c02790.png)
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Abstract
En 中文
Global optimization of bimetallic and monometallic cluster structures remains computationally challenging, particularly due to the rapid increase in homotops with system size and compositional complexity. To address this issue, we present a Collaborative Differential Evolution (CDE) algorithm featuring a multisubpopulation collaborative architecture specifically designed for efficient structure prediction of diverse nanocluster systems. The framework integrates three functionally specialized subpopulations for exploration, exploitation, and balance along with adaptive operations tailored for metallic nanoclusters. This algorithm is implemented as a user-friendly online C++ toolkit. We demonstrate the versatility and robustness of our approach through comprehensive structural optimization across three distinct case studies: Pt–Pd and Cu–Au bimetallic clusters, as well as monometallic Pt clusters. The CDE algorithm consistently achieves 50–100% faster convergence and superior stability compared to conventional methods across all tested systems, establishing itself as a robust and generalizable tool for accelerating the discovery of stable configurations in diverse cluster materials.
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