Ab initio phonon transport across grain boundaries in graphene using machine learning based on small dataset

Ab initio phonon transport across grain boundaries in graphene using machine learning based on small dataset

Establishing the structure-property relationship for grain boundaries (GBs) is critical for developing next-generation functional materials but has been severely hampered due to its extremely large configurational space. Atomistic simulations with low computational cost and high predictive power are...

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Bibliographic Details
Main Authors: Esfarjani, K. (Author), Guo, R. (Author), Hashemi, A. (Author), Lee, S. (Author)
Format: Article
Language:English
Published: American Physical Society 2022
Subjects:
Ab initio
Computation theory
Computational costs
Configurational spaces
Cost effectiveness
Density functional theory
Dislocations densities
Functional materials
Grain boundaries
Grain-boundaries
Graphene
Interatomic potential
Machine learning
Phonons
Predictive power
Single crystals
Structural unit
Thermal transport
Training dataset
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