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

Hashemi, Amirreza and Guo, Ruiqiang and Esfarjani, Keivan and Lee, Sangyeop (2021) Ab initio phonon transport across grain boundaries in graphene using machine learning based on small dataset. (Submitted)

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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 strongly desirable, but the conventional simulations using empirical interatomic potentials and density functional theory suffer from the lack of predictive power and high computational cost, respectively. A machine learning interatomic potential (MLIP) recently emerged but often requires an extensive size of the training dataset, making it a less feasible approach. Here we demonstrate that an MLIP trained with a rationally designed small training dataset can predict thermal transport across GBs in graphene with ab initio accuracy at an affordable computational cost. In particular, we employed a rational approach based on the structural unit model to find a small set of GBs that can represent the entire configurational space and thus can serve as a cost-effective training dataset for the MLIP. Only 5 GBs were found to be enough to represent the entire configurational space of graphene GBs. Using the atomistic Green’s function approach and the MLIP, we revealed that the structure-thermal resistance relation in graphene does not follow the common understanding that large dislocation density causes larger thermal resistance. In fact, thermal resistance is nearly independent of dislocation density at room temperature and is higher when the dislocation density is small at sub-room temperature. We explain this intriguing behavior with the buckling near a GB causing a strong scattering of flexural phonon modes. Our work shows that a machine learning technique combined with conventional wisdom (e.g., structural unit model) can extend the recent success of ab initio thermal transport simulation, which has been mostly limited to single crystals, to complex yet practically important polycrystals with GBs.


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Item Type: Article
Status: Submitted
CreatorsEmailPitt UsernameORCID
Hashemi, Amirrezaamh299@pitt.eduamh299
Lee, Sangyeopsylee@pitt.edusylee
Date: 10 November 2021
Schools and Programs: Swanson School of Engineering > Mechanical Engineering and Materials Science
Refereed: No
Article Type: Research Article
Date Deposited: 11 Nov 2021 19:20
Last Modified: 11 Nov 2021 19:20


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