Accessing complex reconstructed material structures with hybrid global optimization accelerated via on-the-fly machine learning
CHEMICAL SCIENCE(2023)
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 x 4) periodicity. The obtained structures are consistent with experimental results and are energetically lower than the previously presented model.
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Key words
complex reconstructed material structures,hybrid global optimization,machine learning,on-the-fly
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