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Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

Enhancing global optimization for bimetallic clusters: an adaptive multi-strategy differential evolution algorithm.

Xiaomin Wu¹, Miao He², Yousi Lin¹

¹School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Xiamen, China. wuxiaomin@xmut.edu.cn.

|November 10, 2025

Summary

This summary is machine-generated.

We developed an adaptive multi-strategy differential evolution (AMSDE) algorithm to find the lowest-energy structures of bimetallic clusters. This new method offers faster convergence and improved stability for nanoalloy design.

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Area of Science:

Computational chemistry and materials science
Algorithm development for complex systems

Background:

Identifying stable low-energy structures of bimetallic clusters is crucial for designing advanced catalytic materials.
Traditional optimization algorithms often suffer from premature convergence to local minima, limiting their effectiveness.

Purpose of the Study:

To develop an efficient and robust algorithm for determining the global minimum energy structures of bimetallic clusters.
To investigate the structural, energetic, and electronic properties of platinum-nickel (Pt-Ni) clusters.

Main Methods:

Development of an adaptive multi-strategy differential evolution (AMSDE) algorithm.
AMSDE employs adaptive population sizing and three cooperative, dynamically evolving populations for global exploration, local exploitation, and diversity preservation.
Systematic evaluation of Pt-Ni clusters (3-23 atoms) using the AMSDE framework.

Main Results:

The AMSDE algorithm demonstrates faster convergence, higher stability, and lower energy compared to traditional methods.
Analysis revealed detailed energy, structural stability, and electronic properties of small and medium-sized Pt-Ni clusters.
Identified structural preferences and stability trends in Pt-Ni clusters, including magic-number configurations.

Conclusions:

The AMSDE algorithm provides an efficient framework for structural optimization of nanoalloy clusters.
The study offers atomistic insights into the behavior of Pt-Ni clusters, aiding in the rational design of catalytic materials.