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 2023
Accepted
13 7 2023
First published
20 7 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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