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GMMLP: An Efficient Software for Searching the Global-Minimum of Clusters Accelerated by Using the Machine Learning

Yang-Yang Zhang1,2, Yu Cheng1,2, Shu-Wen Zhang1,2

  • 1Fundamental Science Center of Rare Earths, Ganjiang Innovation Academy, Chinese Academy of Sciences, Ganzhou, Jiangxi, China.

Journal of Computational Chemistry
|May 7, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces GMMLP, a machine learning-powered software for efficiently finding the global minimum structures of chemical clusters. It significantly speeds up the search for complex cluster configurations.

Keywords:
GMMLPclustersglobal‐minimummachine learningsoftware

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Area of Science:

  • Computational Chemistry
  • Materials Science
  • Chemical Physics

Background:

  • Finding the global-minimum (GM) structure of clusters is computationally challenging due to the vast number of local minima on the potential energy surface (PES).
  • Existing methods struggle with the exponential increase in complexity as cluster size grows.

Purpose of the Study:

  • To develop an efficient software package, GMMLP (Global-Minimum Search of Clusters Accelerated by Machine Learning Potentials), for identifying the GM structures of clusters.
  • To leverage machine learning potentials for accelerating the global search of cluster structures.

Main Methods:

  • GMMLP integrates the atom-in-molecules neural network potential (AIMNet2) with an improved genetic algorithm (GA).
  • AIMNet2 was trained at the ωB97M-D3/def2-TZVPP level of theory, ensuring high accuracy.
  • The optimized GA provides robust global search capabilities.

Main Results:

  • Benchmark tests on nine types of clusters (n=1-10) demonstrated GMMLP's efficiency in exploring the PES.
  • GMMLP searched 9869 isomers in approximately 10.8 hours, with average times per isomer ranging from 0.22s to 10.01s.
  • Analysis of relative energies and optimized structures confirmed the reliability of the search process.

Conclusions:

  • GMMLP offers a powerful and efficient tool for accurate GM structure identification in diverse cluster systems.
  • This capability is crucial for advancing research in cluster properties and their applications.
  • The software accelerates discovery in chemistry, materials science, and related fields.