Accessing complex reconstructed material structures with hybrid global optimization accelerated via on-the-fly machine learning.
Chem Sci
; 14(33): 8777-8784, 2023 Aug 23.
Article
em En
| MEDLINE
| ID: mdl-37621421
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.
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01-internacional
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MEDLINE
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article