Issue 33, 2023

Accessing complex reconstructed material structures with hybrid global optimization accelerated via on-the-fly machine learning

Abstract

The complex reconstructed structure of materials can be revealed by global optimization. This paper describes a hybrid evolutionary algorithm (HEA) that combines differential evolution and genetic algorithms with a multi-tribe framework. An on-the-fly machine learning calculator is adopted to expedite the identification of low-lying structures. With a superior performance to other well-established methods, we further demonstrate its efficacy by optimizing the complex oxidized surface of Pt/Pd/Cu with different facets under (4 × 4) periodicity. The obtained structures are consistent with experimental results and are energetically lower than the previously presented model.

Graphical abstract: Accessing complex reconstructed material structures with hybrid global optimization accelerated via on-the-fly machine learning

Supplementary files

Article information

Article type
Edge Article
Submitted
12 6月 2023
Accepted
13 7月 2023
First published
20 7月 2023
This article is Open Access

All publication charges for this article have been paid for by the Royal Society of Chemistry
Creative Commons BY license

Chem. Sci., 2023,14, 8777-8784

Accessing complex reconstructed material structures with hybrid global optimization accelerated via on-the-fly machine learning

X. Shi, D. Cheng, R. Zhao, G. Zhang, S. Wu, S. Zhen, Z. Zhao and J. Gong, Chem. Sci., 2023, 14, 8777 DOI: 10.1039/D3SC02974C

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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