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C++ Toolkit for Bimetallic Cluster Structure Optimization Using Collaborative Differential Evolution.

Xiaomin Wu1, Miao He2, Yousi Lin1

  • 1School of Optoelectronic and Communication Engineering, Xiamen University of Technology, Xiamen 361024, China.

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|February 4, 2026
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Summary
This summary is machine-generated.

We developed a Collaborative Differential Evolution (CDE) algorithm for efficient nanocluster structure prediction. This method accelerates the discovery of stable configurations for bimetallic and monometallic clusters.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Nanotechnology

Background:

  • Global optimization of bimetallic and monometallic cluster structures is computationally intensive.
  • The number of possible configurations (homotops) increases rapidly with system size and complexity.

Purpose of the Study:

  • To develop an efficient algorithm for predicting the stable structures of nanocluster systems.
  • To address the computational challenges in global optimization of diverse cluster structures.

Main Methods:

  • Introduction of a Collaborative Differential Evolution (CDE) algorithm.
  • Utilizing a multisubpopulation collaborative architecture with specialized subpopulations for exploration, exploitation, and balance.
  • Implementation of adaptive operations tailored for metallic nanoclusters and availability as an online C++ toolkit.

Main Results:

  • Demonstrated versatility and robustness through structural optimization of Pt-Pd, Cu-Au bimetallic, and monometallic Pt clusters.
  • Achieved 50-100% faster convergence compared to conventional methods.
  • Showcased superior stability in structural predictions across all tested systems.

Conclusions:

  • The CDE algorithm is a robust and generalizable tool for accelerating the discovery of stable configurations in diverse cluster materials.
  • The developed toolkit provides an efficient and user-friendly solution for nanocluster structure prediction.