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Toward Low-Symmetry Systems: An Adaptive Differential Evolution Algorithm for Global Structure Searching.
Zhang, Yehui; Wang, Bing; Ouyang, Yixin; Zhou, Yipeng; Li, Qiang; Wang, Jinlan.
Afiliação
  • Zhang Y; School of Physics, Southeast University, Nanjing 211189, China.
  • Wang B; Institute for Computational Materials Science, School of Physics and Electronics, Henan University, Kaifeng 475004, China.
  • Ouyang Y; School of Physics, Southeast University, Nanjing 211189, China.
  • Zhou Y; School of Physics, Southeast University, Nanjing 211189, China.
  • Li Q; School of Physics, Southeast University, Nanjing 211189, China.
  • Wang J; School of Physics, Southeast University, Nanjing 211189, China.
J Phys Chem Lett ; 13(13): 2986-2993, 2022 Apr 07.
Article em En | MEDLINE | ID: mdl-35343697
ABSTRACT
The reduction in the symmetry of nanomaterials can produce unexpected properties, while the determination of atomic structures is a sizable challenge in related fields, including low-dimensional materials, surface science, defects, etc. Herein, we develop an adaptive algorithm based on the differential evolution algorithm, which provides benefits for structure searching on low-symmetry systems. The dynamic strategy pool and the island concept are proposed to accelerate the efficiency in the full search space. With several test examples, the designed program not only locates reported structures but also affords new stable configurations that were not located by previous structure search algorithms. Moreover, we provide frameworks and interfaces for stable structure searching on complex systems like grain boundaries, supported clusters, surfaces, and edges. The success in repeatable structure searching with high efficiency demonstrates the reliability and practicability of our algorithm and ensures its potential applications as an advanced technology in many newly arising fields.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article