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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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NWPEsSe: An Adaptive-Learning Global Optimization Algorithm for Nanosized Cluster Systems.

Jun Zhang1, Vassiliki-Alexandra Glezakou1, Roger Rousseau1

  • 1Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States.

Journal of Chemical Theory and Computation
|May 5, 2020
PubMed
Summary
This summary is machine-generated.

A new algorithm enhances global optimization for chemical systems, finding stable structures in catalysis and materials science. This powerful tool, Northwest Potential Energy Search Engine (NWPEsSe), is freely available for nanosized cluster research.

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Area of Science:

  • Computational Chemistry
  • Materials Science
  • Chemical Physics

Background:

  • Global optimization is crucial for understanding chemical systems like catalysis and materials.
  • Accurate structure prediction is essential for designing new materials and processes.

Purpose of the Study:

  • To develop a novel, adaptive algorithm for global optimization of large chemical systems.
  • To provide a powerful and efficient tool for predicting the lowest energy structures of nanosized clusters.

Main Methods:

  • An updated artificial bee colony (ABC) algorithm with adaptive learning was employed.
  • The algorithm was tested on diverse systems: gas-phase, ligated, and supported clusters.
  • Systems included Au55, Au82+, Au8 on graphene oxide/rutile, and a Co6Te8(PEt3)6[C60] assembly.

Main Results:

  • Reliable global minima (GMs) were obtained for all tested systems.
  • Published data was confirmed, and new lower-energy structures were identified.
  • The algorithm successfully optimized systems with up to 1300 atoms and sizes up to 3 nm.

Conclusions:

  • The novel ABC-based algorithm provides a powerful and efficient approach for global optimization of nanosized clusters.
  • The freely available program, NWPEsSe, facilitates research in catalysis, materials science, and separations.
  • The method demonstrates broad applicability across various chemical systems and cluster types.