Small-data-based machine learning interatomic potentials for graphene grain boundaries enabled by structural unit model

Machine learning interatomic potentials (MLIPs) are emerging as a powerful tool to achieve efficient atomistic simulations with DFT-level accuracy, which can greatly enhance the capability of modeling more realistic systems. However, training high-quality MLIPs often requires a big database typicall...

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Detalles Bibliográficos

Principais autores:	Ruiqiang Guo, Guotai Li, Jialin Tang, Yinglei Wang, Xiaohan Song
Formato:	Artigo
Idioma:	Inglês
Publicado:	Elsevier 2023-06-01
Series:	Carbon Trends
Assuntos:	Machine learning interatomic potential Structural unit model Grain boundary Phonon transport Thermal conductivity
Acceso en liña:	http://www.sciencedirect.com/science/article/pii/S2667056923000159
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