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A Global Optimizer for Nanoclusters.

Maya Khatun1, Rajat Shubhro Majumdar1, Anakuthil Anoop1

  • 1Department of Chemistry, Indian Institute of Technology Kharagpur, Kharagpur, India.

Frontiers in Chemistry
|October 16, 2019
PubMed
Summary
This summary is machine-generated.

We developed an algorithm to automatically find low-energy nanocluster structures. This PyAR program uses an evolutionary growth strategy to generate unique minimum energy geometries for various clusters.

Keywords:
PyARbinary clustercluster builderglobal optimizationnanoalloysnanoclusterternary cluster

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Area of Science:

  • Computational chemistry
  • Materials science
  • Nanotechnology

Background:

  • Determining the lowest energy structures of nanoclusters is crucial for understanding their properties.
  • Existing methods often struggle with the complexity and vast search space of possible cluster geometries.

Purpose of the Study:

  • To develop an automated algorithm for identifying global and low-energy minima of nanoclusters.
  • To implement this algorithm in a user-friendly program (PyAR) for broader application.

Main Methods:

  • A recursive, evolutionary growth strategy builds larger clusters from smaller ones.
  • Trial geometries are generated using a Tabu list to avoid redundancy.
  • Gradient-based local optimization refines candidate structures.

Main Results:

  • The PyAR program successfully generated unique minimum energy geometries for various homometallic (Pd, Pt, Au, Al), binary (Ru-Pt, Au-Pt), and ternary (Ag-Au-Pt) clusters.
  • Analysis of key parameters like relative energy, binding energy, and mixing energy confirmed the validity of the generated structures.

Conclusions:

  • The developed algorithm and PyAR program provide an efficient and automated approach for nanocluster structure prediction.
  • This method facilitates the exploration of low-energy configurations, aiding in the design and understanding of novel nanomaterials.