Accessing negative Poisson's ratio of graphene by machine learning interatomic potentials.

Wu, Jing; Zhou, E; Qin, Zhenzhen; Zhang, Xiaoliang; Qin, Guangzhao

Wu, Jing; Zhou, E; Qin, Zhenzhen; Zhang, Xiaoliang; Qin, Guangzhao.

Afiliación

Wu J; State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, People's Republic of China.
Zhou E; State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, People's Republic of China.
Qin Z; International Laboratory for Quantum Functional Materials of Henan, and School of Physics and Microelectronics, Zhengzhou University, Zhengzhou 450001, People's Republic of China.
Zhang X; Key Laboratory of Ocean Energy Utilization and Energy Conservation of Ministry of Education, School of Energy and Power Engineering, Dalian University of Technology, Dalian 116024, People's Republic of China.
Qin G; State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, People's Republic of China.

Nanotechnology ; 33(27)2022 Apr 20.

Article en En | MEDLINE | ID: mdl-35276687

Palabras clave

graphene; machine learning interatomic potentials; negative Poisson's ratio

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Nanotechnology Año: 2022 Tipo del documento: Article Pais de publicación: Reino Unido

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